code stringlengths 101 5.91M |
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def CalculateCompositionSolventAccessibility(ProteinSequence):
result = CalculateComposition(ProteinSequence, _SolventAccessibility, '_SolventAccessibility')
return result |
def get_datasets(cfg, args):
tr_dataset = DeformHandlesDataset(cfg, cfg.train, train=True)
te_dataset = DeformHandlesDataset(cfg, cfg.val, train=False)
return (tr_dataset, te_dataset) |
def parse_doc(doc, disable, keep_whitespace=False):
disable = ({'ner', 'parser', 'tagger', 'lemmatizer'} if (not disable) else disable)
for (position, sent) in enumerate(doc.sents):
parts = defaultdict(list)
for (i, token) in enumerate(sent):
text = str(sent.text)
parts['newlines'] = [m.span()[0] for m in re.finditer('(\\n)', text)]
if ((not keep_whitespace) and (not token.text.strip())):
continue
parts['words'].append(token.text)
parts['abs_char_offsets'].append(token.idx)
if ('lemmatizer' not in disable):
parts['lemmas'].append(token.lemma_)
if ('tagger' not in disable):
parts['pos_tags'].append(token.tag_)
if ('ner' not in disable):
parts['ner_tags'].append((token.ent_type_ if token.ent_type_ else 'O'))
if ('parser' not in disable):
head_idx = (0 if (token.head is token) else ((token.head.i - sent[0].i) + 1))
parts['dep_parents'].append(head_idx)
parts['dep_labels'].append(token.dep_)
if (not parts['words']):
continue
parts['i'] = position
(yield parts) |
class ContextualBandit(object):
def __init__(self, context_dim, num_actions):
self._context_dim = context_dim
self._num_actions = num_actions
def feed_data(self, data):
if (data.shape[1] != (self.context_dim + self.num_actions)):
raise ValueError('Data dimensions do not match.')
self._number_contexts = data.shape[0]
self.data = data
self.order = range(self.number_contexts)
def reset(self):
self.order = np.random.permutation(self.number_contexts)
def context(self, number):
return self.data[self.order[number]][:self.context_dim]
def reward(self, number, action):
return self.data[self.order[number]][(self.context_dim + action)]
def optimal(self, number):
return np.argmax(self.data[self.order[number]][self.context_dim:])
def context_dim(self):
return self._context_dim
def num_actions(self):
return self._num_actions
def number_contexts(self):
return self._number_contexts |
def benchmark(repetitions, timeout):
for fileName in os.listdir(PROJECT_CONFIG['build_dir']):
try:
conf = Configuration.get_conf(fileName)
except ValueError:
continue
confStr = conf.to_string()
folderName = conf.benchmark_folder()
kernelFolder = os.path.join(PROJECT_CONFIG['build_dir'], fileName)
kernelString = PROJECT_CONFIG['kernel_file']
kernelPath = os.path.join(kernelFolder, (kernelString + '.xclbin'))
if (not os.path.exists(kernelPath)):
continue
benchmarkFile = 'benchmark.csv'
print('Running {}...'.format(confStr))
if (run_process(['make'], kernelFolder, pipe=False) != 0):
raise Exception((confStr + ': software build failed.'))
repsDone = 0
timeouts = 0
with open(benchmarkFile, 'a') as outFile:
outFile.write((conf.csv_header() + ',time,performance,power,power_efficiency\n'))
while (repsDone < repetitions):
time.sleep(0.5)
profilePath = os.path.join(('benchmark_' + str(datetime.datetime.now()).replace(' ', '_').replace(':', '-')))
print('Running iteration {} / {}...'.format((repsDone + 1), repetitions))
cmd = ['./RunHardware.exe']
if ('DYNAMIC_SIZES=ON' in open(os.path.join(kernelFolder, 'configure.sh')).read()):
cmd += [str(conf.size_n), str(conf.size_k), str(conf.size_m)]
cmd += ['hw', 'off']
try:
ret = run_process(cmd, kernelFolder, pipe=True, logPath=profilePath, timeout=timeout)
except sp.TimeoutExpired as err:
timeouts += 1
if (timeouts > 10):
print((('\n' + confStr) + ': exceeded maximum number of timeouts. Skipping.'))
break
else:
print((confStr + ': timeout occurred. Retrying...'))
continue
if (ret != 0):
raise Exception((confStr + ': kernel execution failed.'))
repsDone += 1
timeouts = 0 |
def get_parser():
parser = ArgumentParser(description='NN Explainer', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add('-c', '--config', is_config_file=True, help='config file path')
parser.add('--arg_log', default=False, type=str2bool, help='save arguments to config file')
parser.add_argument('--dataset', choices=['VOC', 'COCO', 'CUB'], default='VOC', type=str, help='which dataset to use')
parser.add_argument('--data_base_path', default='../datasets/', type=str, help='Base bath of the datasets. Should contain subdirectories with the different datasets.')
parser.add_argument('--train_batch_size', default=16, type=int, help='batch size used for training')
parser.add_argument('--val_batch_size', default=16, type=int, help='batch size used for validation')
parser.add_argument('--test_batch_size', default=16, type=int, help='batch size used for testing')
parser.add_argument('--use_data_augmentation', default=False, type=str2bool, help='set to true to enable data augmentation on training images')
parser.add_argument('--seed', default=42, type=int, help='seed for all random number generators in pytorch, numpy, and python.random')
parser.add_argument('--use_tensorboard_logger', default=False, type=str2bool, help='whether to use tensorboard')
parser.add_argument('--checkpoint_callback', default=True, type=str2bool, help='if true, trained model will be automatically saved')
parser.add_argument('--early_stop_min_delta', default=0.001, type=float, help='threshold for early stopping condition')
parser.add_argument('--early_stop_patience', default=5, type=int, help='patience for early stopping to trigger')
parser.add_argument('--train_model', default=True, type=str2bool, help='If True, specified model will be trained. If False, model will be tested.')
parser.add_argument('--use_imagenet_pretraining', default=True, type=str2bool, help='If True, classifiers use a pretrained backbone from ImageNet pretraining')
parser.add_argument('--fix_classifier_backbone', default=True, type=str2bool, help='Whether to fix the wait for the classifiers backbone')
parser.add_argument('--fix_classifier', default=True, type=str2bool, help='If True, classifier is frozen. Strongly recommended for Explainer training.')
parser.add_argument('--model_to_train', choices=['explainer', 'classifier', 'fcnn', 'rtsal_explainer'], default='explainer', type=str, help='which model architecture should be used for training or testing')
parser.add_argument('--classifier_type', choices=['vgg16', 'resnet50'], default='vgg16', type=str, help='type of classifier architecture to use')
parser.add_argument('--explainer_classifier_checkpoint', default=None, type=str, help='Path to the .ckpt file that contains the weights of a pretrained explainer. Also contains the weights for the associated classifier.')
parser.add_argument('--classifier_checkpoint', default=None, type=str, help='Path to the .ckpt file that contains the weights of a pretrained classifier.')
parser.add_argument('--fcnn_checkpoint', default=None, type=str, help='Path to the .ckpt file that contains the weights of a pretrained self-explainer.')
parser.add_argument('--learning_rate', default=1e-05, type=float, help='learning rate used by the Adam optimizer')
parser.add_argument('--use_mask_variation_loss', default=True, type=str2bool, help='whether to use variation loss on the mask.')
parser.add_argument('--use_mask_area_loss', default=True, type=str2bool, help='whether to use area loss on the mask.')
parser.add_argument('--use_mask_coherency_loss', default=True, type=str2bool, help='whether to use mask coherency loss (only for self-explainer architecture)')
parser.add_argument('--entropy_regularizer', default=1.0, type=float, help='loss weighting term for entropy loss')
parser.add_argument('--mask_variation_regularizer', default=1.0, type=float, help='loss weighting term for mask variation loss')
parser.add_argument('--mask_area_constraint_regularizer', default=1.0, type=float, help='loss weighting term for overall mask area constraint (currently not used!)')
parser.add_argument('--mask_total_area_regularizer', default=0.1, type=float, help='loss weighting term for the total area loss')
parser.add_argument('--ncmask_total_area_regularizer', default=0.3, type=float, help='loss weighting term for the area constraints for the individual class segmentation masks')
parser.add_argument('--target_mask_min_area', default=0.05, type=float, help='minimum area for the overall mask area constraint (currently not used!)')
parser.add_argument('--target_mask_max_area', default=0.5, type=float, help='maximum area for the overall mask area constraint (currently not used!)')
parser.add_argument('--class_mask_min_area', default=0.05, type=float, help='minimum area for the area constraints for the individual class segmentation masks')
parser.add_argument('--class_mask_max_area', default=0.3, type=float, help='maximum area for the area constraints for the individual class segmentation masks')
parser.add_argument('--show_images', default=False, type=str2bool, help='If true, displays images and corresponding masked images during testing. Requires testing batch size to be 1.')
parser.add_argument('--show_all_class_masks', default=False, type=str2bool, help='If true, displays individual class masks during testing. Requires VOC dataset. Requires testing batch size to be 1.')
parser.add_argument('--show_max_activation_for_class_id', default=None, type=int, help='If true, highlights point of maximum activation for given class id. Requires testing batch size to be 1.')
parser.add_argument('--save_masks', default=False, type=str2bool, help='If true, masks are saved to location specified by save_path (see below)')
parser.add_argument('--save_masked_images', default=False, type=str2bool, help='If true, masked images are saved to location specified by save_path (see below)')
parser.add_argument('--save_all_class_masks', default=False, type=str2bool, help='Unused.')
parser.add_argument('--save_path', default='./results/', type=str, help='Path to where masks and/or masked images are saved if corresponding options are set to true.')
parser.add_argument('--metrics_threshold', default=(- 1.0), type=float, help='Threshold for logit to count as positive vs. negative prediction. Use -1.0 for Explainer and 0.0 for classifier.')
return parser |
def get_eval_instances(handles, listener=False):
insts = [(Instance(input=name, output=tuple(color)) if listener else Instance(input=tuple(color), output=name)) for (name, handle) in handles.iteritems() for color in munroecorpus.open_datafile(handle)]
rng.shuffle(insts)
return insts |
def map_utt_doc(conv, wiki):
(document, utterances) = ([], [])
prev_uid = None
for utt in conv:
sec_idx = utt['docIdx']
sec = wiki[sec_idx]
curr_text = utt['text'].strip('\n').strip('\t').lower()
curr_uid = utt['uid']
if (prev_uid == None):
prev_uid = curr_uid
document.append(sec)
elif (curr_uid == prev_uid):
prev_utt = utterances.pop((- 1))
curr_text = ((prev_utt + ' ') + curr_text)
else:
prev_uid = curr_uid
document.append(sec)
utterances.append(curr_text)
return (document, utterances) |
def get_models(keyword, softwares=None):
if (softwares is None):
softwares = ['obj']
per_page = 100
(model_names, images, total_models) = search(keyword, per_page=per_page, softwares=softwares)
insert_search_log(keyword, total_models, softwares)
for i in tqdm(range((total_models // per_page))):
(model_name, image, _) = search(keyword, page=(i + 2), softwares=softwares)
model_names += model_name
images += image
print(f'{total_models} models found.')
return (model_names, images) |
class Contrast(BaseAugmentation):
def _augment(self, img):
v = (float_parameter(self.level, 1.8) + 0.1)
return ImageEnhance.Contrast(img).enhance(v) |
def __getattr__(name):
return _sub_module_deprecation(sub_package='sparse.linalg', module='interface', private_modules=['_interface'], all=__all__, attribute=name) |
class _Aggregation(enum.Enum):
NONE = 0
MAX = 1
MIN = 2
COUNT = 3
SUM = 4
AVERAGE = 5 |
_dispatch
def ihfftn(x, s=None, axes=None, norm=None, overwrite_x=False, workers=None):
return (Dispatchable(x, np.ndarray),) |
def save_obj(f, verts, faces, decimal_places: Optional[int]=None, verts_uvs: Optional[list]=None, faces_uvs: Optional[list]=None, texture_map: Optional[list]=None):
use_texture = ((verts_uvs is not None) and (texture_map is not None) and (faces_uvs is not None))
if use_texture:
output_path = pathlib.Path(f)
obj_header = '\nmtllib {0}.mtl\nusemtl mesh\n\n'.format(output_path.stem)
if (len(verts) and (not ((verts.dim() == 2) and (verts.size(1) == 3)))):
message = "Argument 'verts' should either be empty or of shape (num_verts, 3)."
raise ValueError(message)
if (len(faces) and (not ((faces.dim() == 2) and (faces.size(1) == 3)))):
message = "Argument 'faces' should either be empty or of shape (num_faces, 3)."
raise ValueError(message)
new_f = False
if isinstance(f, str):
new_f = True
f = open(f, 'w')
elif isinstance(f, pathlib.Path):
new_f = True
f = f.open('w')
try:
if use_texture:
f.write(obj_header)
_save(f, verts, faces, decimal_places, verts_uvs, faces_uvs)
finally:
if new_f:
f.close()
new_f = False
if use_texture:
try:
transforms.ToPILImage()(texture_map.squeeze().cpu().permute(2, 0, 1)).save((output_path.parent / (output_path.stem + '.png')))
f_mtl = open((output_path.parent / (output_path.stem + '.mtl')), 'w')
new_f = True
lines = f'''newmtl mesh
map_Kd {output_path.stem}.png
# Test colors
Ka 1.000 1.000 1.000 # white
Kd 1.000 1.000 1.000 # white
Ks 0.000 0.000 0.000 # black
Ns 10.0
'''
f_mtl.write(lines)
finally:
if new_f:
f_mtl.close() |
class FakeExpression():
def __init__(self, operands, operator):
self._operands = operands
self._operator = operator
def __repr__(self):
return ('FakeExpression(%r, %r)' % (self._operands, self._operator))
def pyobject(self):
raise TypeError('self must be a numeric expression')
def operands(self):
return self._operands
def __getitem__(self, i):
return self._operands[i]
def operator(self):
return self._operator
def _fast_callable_(self, etb):
return fast_callable(self, etb) |
class AdjointFdfdSimulation():
def __init__(self, sim: FdfdSimulation) -> None:
self.sim = sim
self.cache = ([None] * len(sim.cache))
self.lock = threading.Lock()
def simulate(self, z: np.ndarray, J: np.ndarray) -> np.ndarray:
with self.lock:
electric_fields = None
for cache_index in range(len(self.cache)):
cache_item = self.cache[cache_index]
if (cache_item is None):
continue
(cache_z, cache_J, cache_fields) = cache_item
if (np.array_equal(z, cache_z) and np.array_equal(J, cache_J)):
electric_fields = cache_fields
del self.cache[cache_index]
break
if (electric_fields is None):
electric_fields = self._run_solver(z, J)
del self.cache[0]
self.cache.append((z, J, electric_fields))
return electric_fields
def _run_solver(self, z: np.ndarray, J: np.ndarray) -> np.ndarray:
dxes = [[np.conj(dx) for dx in grid] for grid in self.sim.dxes]
(spx, spy, spz) = np.meshgrid(dxes[1][0], dxes[1][1], dxes[1][2], indexing='ij')
(sdx, sdy, sdz) = np.meshgrid(dxes[0][0], dxes[0][1], dxes[0][2], indexing='ij')
mult = np.multiply
s = [mult(mult(sdx, spy), spz), mult(mult(spx, sdy), spz), mult(mult(spx, spy), sdz)]
new_J = np.copy(fdfd_tools.unvec(J, self.sim.dims))
for k in range(3):
new_J[k] /= np.conj(s[k])
new_J = fdfd_tools.vec(new_J)
mu = None
if (self.sim.mu is not None):
mu = np.conj(fdfd_tools.vec(self.sim.mu))
sim_args = {'omega': np.conj(self.sim.omega), 'dxes': dxes, 'epsilon': np.conj(self.sim.get_epsilon(z)), 'mu': mu, 'J': new_J, 'pec': fdfd_tools.vec(self.sim.pec), 'pmc': fdfd_tools.vec(self.sim.pmc), 'bloch_vec': self.sim.bloch_vec}
efields = self.sim.solver.solve(**sim_args)
efields = fdfd_tools.unvec(efields, self.sim.dims)
for i in range(3):
efields[i] = np.multiply(efields[i], np.conj(s[i]))
efields = fdfd_tools.vec(efields)
return efields |
def residual_block(input, output_shape, is_train, info=False, k=3, s=1, name='residual', activation_fn=lrelu, batch_norm=True):
with tf.variable_scope(name):
with tf.variable_scope('res1'):
_ = conv2d(input, output_shape, is_train, k_h=k, k_w=k, s=s, activation_fn=activation_fn, batch_norm=batch_norm)
with tf.variable_scope('res2'):
_ = conv2d(input, output_shape, is_train, k_h=k, k_w=k, s=s, activation_fn=None, batch_norm=batch_norm)
_ = activation_fn((_ + input))
if info:
log.info('{} {}'.format(name, _))
return _ |
def extract(filename, node, prefix):
if (not ((node.location.file is None) or os.path.samefile(d(node.location.file.name), filename))):
return 0
if (node.kind in RECURSE_LIST):
sub_prefix = prefix
if (node.kind != CursorKind.TRANSLATION_UNIT):
if (len(sub_prefix) > 0):
sub_prefix += '_'
sub_prefix += d(node.spelling)
for i in node.get_children():
extract(filename, i, sub_prefix)
if (node.kind in PRINT_LIST):
comment = (d(node.raw_comment) if (node.raw_comment is not None) else '')
comment = process_comment(comment)
sub_prefix = prefix
if (len(sub_prefix) > 0):
sub_prefix += '_'
if (len(node.spelling) > 0):
name = sanitize_name((sub_prefix + d(node.spelling)))
global output
output.append((name, filename, comment)) |
class NetGradientChecker(object):
def CompareNets(nets, outputs, outputs_with_grad_ids, inputs_with_grads, input_values=None, threshold=1e-07, print_net_images=False):
def _get_output_with_grad_names(net_outputs):
return [net_outputs[i] for i in outputs_with_grad_ids]
if print_net_images:
for (i, net) in enumerate(nets):
png = net_drawer.GetPydotGraph(net).create_png()
with open(((('caffe2_net_forward_' + str(i)) + net.Name()) + '.png'), 'wb') as f:
f.write(png)
results = [_get_grad(net, net_outputs, _get_output_with_grad_names(net_outputs), input_values, inputs_with_grads) for (net, net_outputs) in zip(nets, outputs)]
if print_net_images:
(_, _, backward_nets) = zip(*results)
for (i, net) in enumerate(backward_nets):
png = net_drawer.GetPydotGraph(net).create_png()
with open(((('caffe2_net_' + str(i)) + net.Name()) + '.png'), 'wb') as f:
f.write(png)
(first_net_results, first_net_grads, _) = results[0]
for (net_results, net_grads, _) in results[1:]:
assert (len(net_results) == len(first_net_results))
for (idx, ((blob1, blob_value1), (blob2, blob_value2))) in enumerate(zip(first_net_results, net_results)):
_assert_close(blob_value1, blob_value2, threshold, err_msg='Different forward pass results for output id {}. Corresponding output blobs: {} and {}'.format(idx, blob1, blob2))
assert (net_grads.keys() == first_net_grads.keys())
for (blob, blob_grad_value) in net_grads.items():
_assert_close(first_net_grads[blob], blob_grad_value, threshold, err_msg='Different gradients for input {}'.format(blob))
def Check(net, outputs_with_grad, input_values, input_to_check, step_size=0.0001, threshold=0.05, print_net=True):
(net_results, net_grads, full_net) = _get_grad(net, [], outputs_with_grad, input_values, [input_to_check])
analytic_grad = net_grads[input_to_check]
def GetLoss(new_value):
workspace.blobs[input_to_check] = new_value
workspace.RunNetOnce(full_net)
return sum([workspace.blobs[output] for output in outputs_with_grad]).sum()
def GetValue(dim, delta):
input_value = input_values[input_to_check].copy()
input_value.flat[dim] += delta
return input_value
grad_estimate = np.zeros_like(input_values[input_to_check])
for dim in range(input_values[input_to_check].size):
pos_loss = GetLoss(GetValue(dim, step_size))
neg_loss = GetLoss(GetValue(dim, (- step_size)))
grad_estimate.flat[dim] = (((pos_loss - neg_loss) / step_size) / 2)
err_msg = 'Error in gradient check for net_copy {}'.format(net.Name())
if print_net:
err_msg += ': {}'.format(net.Proto())
return _assert_close(analytic_grad, grad_estimate, threshold, err_msg) |
class GrayReconstruction():
param_names = ['shape', 'dtype']
params = [((10, 10), (64, 64), (1200, 1200), (96, 96, 96)), (np.uint8, np.float32, np.float64)]
def setup(self, shape, dtype):
rng = np.random.default_rng(123)
rvals = rng.integers(1, 255, size=shape).astype(dtype=dtype)
roi1 = tuple((slice((s // 4), (s // 2)) for s in rvals.shape))
roi2 = tuple((slice(((s // 2) + 1), ((3 * s) // 4)) for s in rvals.shape))
seed = np.full(rvals.shape, 1, dtype=dtype)
seed[roi1] = rvals[roi1]
seed[roi2] = rvals[roi2]
mask = np.full(seed.shape, 1, dtype=dtype)
mask[roi1] = 255
mask[roi2] = 255
self.seed = seed
self.mask = mask
def time_reconstruction(self, shape, dtype):
morphology.reconstruction(self.seed, self.mask)
def peakmem_reference(self, *args):
pass
def peakmem_reconstruction(self, shape, dtype):
morphology.reconstruction(self.seed, self.mask) |
class Unet(nn.Module):
def __init__(self, input_channels, num_classes, num_filters, initializers, apply_last_layer=True, padding=True):
super(Unet, self).__init__()
self.input_channels = input_channels
self.num_classes = num_classes
self.num_filters = num_filters
self.padding = padding
self.activation_maps = []
self.apply_last_layer = apply_last_layer
self.contracting_path = nn.ModuleList()
for i in range(len(self.num_filters)):
input = (self.input_channels if (i == 0) else output)
output = self.num_filters[i]
if (i == 0):
pool = False
else:
pool = True
self.contracting_path.append(DownConvBlock(input, output, initializers, padding, pool=pool))
self.upsampling_path = nn.ModuleList()
n = (len(self.num_filters) - 2)
for i in range(n, (- 1), (- 1)):
input = (output + self.num_filters[i])
output = self.num_filters[i]
self.upsampling_path.append(UpConvBlock(input, output, initializers, padding))
if self.apply_last_layer:
self.last_layer = nn.Conv2d(output, num_classes, kernel_size=1)
def forward(self, x, val):
blocks = []
for (i, down) in enumerate(self.contracting_path):
x = down(x)
if (i != (len(self.contracting_path) - 1)):
blocks.append(x)
for (i, up) in enumerate(self.upsampling_path):
x = up(x, blocks[((- i) - 1)])
del blocks
if val:
self.activation_maps.append(x)
if self.apply_last_layer:
x = self.last_layer(x)
return x |
class ProtocolServer(Protocol):
_req_received = None
def __init__(self, request_queue, response_queue):
self.request_queue = request_queue
self.response_queue = response_queue
self._req_received = None
def have_pending_request(self):
return (self._req_received is not None)
def get_new_request(self, block=False):
if self.have_pending_request():
raise Exception('Trying to get next request, while having one unserved')
try:
response = self.request_queue.get(block=block)
except Exception as e:
raise EmptyQueue('queue is empty')
self._req_received = response
return response
def response_reset(self):
if (not self.have_pending_request()):
raise Exception('Attempting to reply with pending request')
if (not isinstance(self._req_received, communication.messages.ResetIteratorRequest)):
raise Exception('Replaying with reset status to other type of message')
self.response_queue.put(communication.messages.ResetIteratorResponse())
self._req_received = None
def response_next(self, value):
if (not self.have_pending_request()):
raise Exception('Attempting to reply with pending request')
self.response_queue.put(communication.messages.GetNextResponse(value))
self._req_received = None
def response_stop(self):
if (not self.have_pending_request()):
raise Exception('Attempting to reply with pending request')
self.response_queue.put(communication.messages.StopIterationResponse())
self._req_received = None
def response_invalid(self):
if (not self.have_pending_request()):
raise Exception('Attempting to reply with pending request')
self.response_queue.put(communication.messages.InvalidStateResponse())
self._req_received = None
def response_terminate(self):
if (not self.have_pending_request()):
raise Exception('Attempting to reply with pending request')
if (not isinstance(self._req_received, communication.messages.TerminateRequest)):
raise Exception('Replaying with terminate status to other type of message')
self.response_queue.put(communication.messages.TerminateResponse())
self._req_received = None |
def test_clip():
default_clipid = '1-104089-A-22'
dataset = esc50.Dataset(TEST_DATA_HOME)
clip = dataset.clip(default_clipid)
expected_attributes = {'audio_path': os.path.join(os.path.normpath('tests/resources/sound_datasets/esc50/'), 'audio/1-104089-A-22.wav'), 'clip_id': '1-104089-A-22'}
expected_property_types = {'filename': str, 'fold': int, 'target': int, 'category': str, 'esc10': bool, 'src_file': str, 'take': str, 'audio': tuple, 'tags': annotations.Tags}
run_clip_tests(clip, expected_attributes, expected_property_types) |
def wavy(ind, k=10.0):
return ((1.0 - (sum(((math.cos((k * ind[i])) * math.exp(((- (ind[i] * ind[i])) / 2.0))) for i in range(len(ind)))) / float(len(ind)))),) |
class TFMPNetForSequenceClassification():
def __init__(self, *args, **kwargs):
requires_tf(self)
def from_pretrained(self, *args, **kwargs):
requires_tf(self) |
class ReduceLR(TrainingBase):
def get_default_config(self):
config = super().get_default_config()
config.update(rlr_factor=0.5, rlr_patience=10, min_lr=1e-06, stopping_lr=0.0, rlr_monitor='val_loss', rlr_monitor_improves_when='less')
return config
def get_default_state(self):
state = super().get_default_state()
state.update(last_rlr_epoch=(- 1))
if (self.config.rlr_monitor_improves_when == 'less'):
state.update(rlr_monitor_value=np.inf, rlr_monitor_epoch=(- 1))
elif (self.config.rlr_monitor_improves_when == 'greater'):
state.update(rlr_monitor_value=0, rlr_monitor_epoch=(- 1))
else:
raise ValueError
return state
def on_epoch_begin(self, logs, training):
super().on_epoch_begin(logs, training)
if ('lr' not in logs):
logs['lr'] = max((group['lr'] for group in self.optimizer.param_groups))
def on_epoch_end(self, logs, training):
super().on_epoch_end(logs, training)
if training:
return
config = self.config
state = self.state
monitor = config.rlr_monitor
try:
new_value = logs[monitor]
new_epoch = logs['epoch']
except KeyError:
print(f'Warning: RLR: COULD NOT FIND LOG!', flush=True)
return
old_value = state.rlr_monitor_value
old_epoch = state.rlr_monitor_epoch
if (((self.config.rlr_monitor_improves_when == 'less') and (new_value <= old_value)) or ((self.config.rlr_monitor_improves_when == 'greater') and (new_value >= old_value))):
state.rlr_monitor_value = new_value
state.rlr_monitor_epoch = new_epoch
elif (config.rlr_factor < 1):
epoch_gap = (new_epoch - max(state.last_rlr_epoch, old_epoch))
if (epoch_gap >= config.rlr_patience):
old_lrs = []
new_lrs = []
for group in self.optimizer.param_groups:
old_lr = group['lr']
new_lr = max((old_lr * config.rlr_factor), config.min_lr)
group['lr'] = new_lr
old_lrs.append(old_lr)
new_lrs.append(new_lr)
old_lr = max(old_lrs)
new_lr = max(new_lrs)
logs['lr'] = new_lr
state.last_rlr_epoch = new_epoch
if self.is_main_rank:
print((f'''
RLR: {monitor} did NOT improve for {epoch_gap} epochs,''' + f' new lr = {new_lr}'), flush=True)
if (new_lr < config.stopping_lr):
if self.is_main_rank:
print(f'''
STOP: lr fell below {config.stopping_lr}, STOPPING TRAINING!''', flush=True)
raise StopTrainingException |
def register_Ns3LteRrcSapSystemInformationBlockType1_methods(root_module, cls):
cls.add_constructor([])
cls.add_constructor([param('ns3::LteRrcSap::SystemInformationBlockType1 const &', 'arg0')])
cls.add_instance_attribute('cellAccessRelatedInfo', 'ns3::LteRrcSap::CellAccessRelatedInfo', is_const=False)
cls.add_instance_attribute('cellSelectionInfo', 'ns3::LteRrcSap::CellSelectionInfo', is_const=False)
return |
def _from_notebook_node(self, nb, resources, **kwargs):
filters = [RTDUrlPreprocessor()]
for f in filters:
(nb, resources) = f.preprocess(nb, resources=resources)
return nbsphinx_from_notebook_node(self, nb, resources=resources, **kwargs) |
def _py_deprecation(ver_python=(3, 6), ver_stardist='0.9.0'):
import sys
from distutils.version import LooseVersion
if ((sys.version_info[:2] == ver_python) and (LooseVersion(__version__) < LooseVersion(ver_stardist))):
print(f'''You are using Python {ver_python[0]}.{ver_python[1]}, which will no longer be supported in StarDist {ver_stardist}.
Please upgrade to Python {ver_python[0]}.{(ver_python[1] + 1)} or later.''', file=sys.stderr, flush=True) |
def read_regl(variables, num_strands=3):
(u, v, w) = variables
data = {}
data[2] = ([3], [[{(0, 1): (- v), (0, 2): 1, (1, 0): 1, (1, 1): u, (2, 1): w}]], [[{(0, 1): 1, (0, 2): ((- u) / w), (1, 2): (1 / w), (2, 0): 1, (2, 2): (v / w)}]])
data[3] = ([24], [[{(0, 1): (- v), (0, 2): 1, (1, 0): 1, (1, 1): u, (2, 1): w, (3, 5): (- v), (3, 7): 1, (4, 6): (- v), (4, 8): 1, (5, 3): 1, (5, 5): u, (6, 4): 1, (6, 6): u, (7, 5): w, (8, 6): w, (9, 9): u, (9, 15): 1, (10, 10): u, (10, 17): 1, (11, 9): w, (12, 10): w, (13, 13): u, (13, 16): 1, (14, 13): w, (15, 9): (- v), (15, 11): 1, (16, 13): (- v), (16, 14): 1, (17, 10): (- v), (17, 12): 1, (18, 19): (- v), (18, 20): 1, (19, 18): 1, (19, 19): u, (20, 19): w, (21, 22): (- v), (21, 23): 1, (22, 21): 1, (22, 22): u, (23, 22): w}, {(0, 3): (- v), (0, 4): 1, (1, 15): (- v), (1, 16): 1, (1, 22): (- v), (1, 23): ((u * v) / w), (2, 17): (- v), (2, 18): 1, (2, 23): ((v * ((u * v) - w)) / w), (3, 0): 1, (3, 3): u, (4, 3): w, (5, 9): (- v), (5, 10): 1, (5, 12): ((- u) / w), (5, 22): u, (5, 23): ((- (u ** 2)) / w), (6, 11): (- v), (6, 22): w, (6, 23): (- u), (7, 12): (((- u) * v) / w), (7, 13): (- v), (7, 19): 1, (7, 21): (- v), (7, 23): (((- (u ** 2)) * v) / w), (8, 12): (- v), (8, 14): (- v), (8, 20): 1, (8, 23): ((- u) * v), (9, 5): 1, (9, 9): u, (9, 23): (u / w), (10, 9): w, (10, 11): (- u), (11, 6): 1, (11, 11): u, (11, 13): u, (12, 13): w, (12, 23): (- v), (13, 23): 1, (14, 8): 1, (14, 14): u, (14, 23): v, (15, 1): 1, (15, 12): (u / w), (15, 15): u, (16, 11): v, (16, 15): w, (16, 23): (- u), (17, 2): 1, (17, 12): ((u * v) / w), (17, 17): u, (18, 12): v, (18, 17): w, (19, 21): w, (19, 23): v, (20, 14): w, (21, 7): 1, (21, 21): u, (21, 23): ((u * v) / w), (22, 11): 1, (23, 12): 1, (23, 23): u}]], [[{(0, 1): 1, (0, 2): ((- u) / w), (1, 2): (1 / w), (2, 0): 1, (2, 2): (v / w), (3, 5): 1, (3, 7): ((- u) / w), (4, 6): 1, (4, 8): ((- u) / w), (5, 7): (1 / w), (6, 8): (1 / w), (7, 3): 1, (7, 7): (v / w), (8, 4): 1, (8, 8): (v / w), (9, 11): (1 / w), (10, 12): (1 / w), (11, 11): (v / w), (11, 15): 1, (12, 12): (v / w), (12, 17): 1, (13, 14): (1 / w), (14, 14): (v / w), (14, 16): 1, (15, 9): 1, (15, 11): ((- u) / w), (16, 13): 1, (16, 14): ((- u) / w), (17, 10): 1, (17, 12): ((- u) / w), (18, 19): 1, (18, 20): ((- u) / w), (19, 20): (1 / w), (20, 18): 1, (20, 20): (v / w), (21, 22): 1, (21, 23): ((- u) / w), (22, 23): (1 / w), (23, 21): 1, (23, 23): (v / w)}, {(0, 3): 1, (0, 4): ((- u) / w), (1, 15): 1, (1, 16): ((- u) / w), (1, 22): ((u * v) / w), (1, 23): ((- u) / w), (2, 17): 1, (2, 18): ((- u) / w), (3, 4): (1 / w), (4, 0): 1, (4, 4): (v / w), (5, 9): 1, (5, 10): ((- u) / w), (5, 13): ((- u) / w), (5, 22): ((- (u ** 2)) / w), (6, 11): 1, (6, 12): ((- u) / w), (6, 13): (((- u) * v) / w), (6, 22): (- u), (7, 19): ((- u) / w), (7, 21): 1, (8, 13): (- v), (8, 14): 1, (8, 20): ((- u) / w), (9, 10): (1 / w), (9, 22): (u / w), (10, 5): 1, (10, 6): ((- u) / w), (10, 10): (v / w), (10, 13): ((- (u ** 2)) / w), (10, 23): (u / w), (11, 22): 1, (12, 13): (- u), (12, 23): 1, (13, 12): (1 / w), (13, 13): (v / w), (14, 20): (1 / w), (15, 13): (u / w), (15, 16): (1 / w), (15, 22): ((- v) / w), (16, 1): 1, (16, 6): (v / w), (16, 13): ((u * v) / w), (16, 16): (v / w), (17, 13): ((u * v) / w), (17, 18): (1 / w), (17, 23): ((- v) / w), (18, 2): 1, (18, 13): v, (18, 18): (v / w), (18, 23): ((- (v ** 2)) / w), (19, 7): 1, (19, 12): (v / w), (19, 19): (v / w), (19, 23): ((u * v) / w), (20, 8): 1, (20, 20): (v / w), (20, 23): v, (21, 13): ((- v) / w), (21, 19): (1 / w), (22, 6): (1 / w), (22, 13): (u / w), (22, 22): (v / w), (23, 13): 1}]])
return data[num_strands] |
class ToricDivisor_generic(Divisor_generic):
def __init__(self, v, parent, check=True, reduce=True):
super().__init__(v, parent, check, reduce)
def _vector_(self, ring=None):
if (ring is None):
ring = self.base_ring()
X = self.parent().scheme()
v = vector(ring, ([0] * X.ngens()))
for (coeff, variable) in self:
v[X.gens().index(variable)] += coeff
return v
def coefficient(self, x):
try:
index = ZZ(x)
variable = self.parent().scheme().gen(index)
except TypeError:
variable = x
for (coeff, var) in self:
if (var == variable):
return coeff
return self.base_ring().zero()
def function_value(self, point):
if (not self.is_QQ_Cartier()):
raise ValueError(('support functions are associated to QQ-Cartier divisors only, %s is not QQ-Cartier' % self))
try:
index = ZZ(point)
return self.coefficient(index)
except TypeError:
pass
fan = self.parent().scheme().fan()
assert (point in fan.lattice()), (('The point ' + str(point)) + ' is not in the N-lattice.')
cone = fan.cone_containing(point)
return (point * self.m(cone))
def m(self, cone):
try:
return self._m[cone]
except AttributeError:
self._m = {}
except KeyError:
pass
X = self.parent().scheme()
M = X.fan().dual_lattice()
fan = X.fan()
cone = fan.embed(cone)
if cone.is_trivial():
m = M(0)
self._m[cone] = m
return m
assert (cone.ambient() is fan)
b = vector((self.coefficient(i) for i in cone.ambient_ray_indices()))
A = cone.rays().column_matrix()
try:
if (cone.dim() == X.dimension()):
m = A.solve_left(b)
else:
(D, U, V) = A.smith_form()
bV = (b * V)
m = (D.solve_left(bV) * U)
except ValueError:
raise ValueError(('%s is not QQ-Cartier, cannot choose a dual vector on %s' % (self, cone)))
try:
m = M(m)
except TypeError:
pass
self._m[cone] = m
return m
def is_Weil(self):
if (self.base_ring() == ZZ):
return True
try:
vector(ZZ, vector(self))
return True
except TypeError:
return False
def is_QQ_Weil(self):
return True
def is_Cartier(self):
try:
return self._is_Cartier
except AttributeError:
pass
self._is_Cartier = self.is_QQ_Cartier()
if self._is_Cartier:
fan = self.parent().scheme().fan()
M = fan.dual_lattice()
self._is_Cartier = all(((self.m(c) in M) for c in fan))
return self._is_Cartier
def is_QQ_Cartier(self):
try:
return self._is_QQ_Cartier
except AttributeError:
pass
try:
[self.m(c) for c in self.parent().scheme().fan()]
self._is_QQ_Cartier = True
except ValueError:
self._is_QQ_Cartier = False
return self._is_QQ_Cartier
def is_integral(self):
return all(((coeff in ZZ) for (coeff, _) in self))
def move_away_from(self, cone):
m = self.m(cone)
X = self.parent().scheme()
fan = X.fan()
if (m in fan.lattice()):
ring = self._ring
else:
ring = m.base_ring()
divisor = list(vector(self))
values = [(mult - (m * ray)) for (mult, ray) in zip(divisor, fan.rays())]
return ToricDivisor(X, values, ring=ring)
def cohomology_class(self):
divisor = vector(self)
variety = self.parent().scheme()
HH = variety.cohomology_ring()
return sum([(divisor[i] * HH.gen(i)) for i in range(HH.ngens())])
def Chern_character(self):
return self.cohomology_class().exp()
ch = Chern_character
def divisor_class(self):
if ('_divisor_class' not in self.__dict__):
self._divisor_class = self.parent().scheme().rational_class_group()(self)
return self._divisor_class
def Chow_cycle(self, ring=ZZ):
toric_variety = self.parent().scheme()
fan = toric_variety.fan()
A = toric_variety.Chow_group(ring)
return sum(((self.coefficient(i) * A(cone_1d)) for (i, cone_1d) in enumerate(fan(dim=1))))
def is_ample(self):
try:
return self._is_ample
except AttributeError:
pass
assert self.is_QQ_Cartier(), 'The divisor must be QQ-Cartier.'
Kc = self.parent().scheme().Kaehler_cone()
self._is_ample = Kc.relative_interior_contains(self.divisor_class())
return self._is_ample
def is_nef(self):
try:
return self._is_nef
except AttributeError:
pass
assert self.is_QQ_Cartier(), 'The divisor must be QQ-Cartier.'
self._is_nef = (self.divisor_class() in self.parent().scheme().Kaehler_cone())
return self._is_nef
def polyhedron(self):
try:
return self._polyhedron
except AttributeError:
pass
fan = self.parent().scheme().fan()
divisor = vector(self)
ieqs = [([divisor[i]] + list(fan.ray(i))) for i in range(fan.nrays())]
self._polyhedron = Polyhedron(ieqs=ieqs)
return self._polyhedron
def sections(self):
try:
return self._sections
except AttributeError:
pass
M = self.parent().scheme().fan().dual_lattice()
self._sections = tuple((M(m) for m in self.polyhedron().integral_points()))
return self._sections
def sections_monomials(self):
return tuple((self.monomial(m) for m in self.sections()))
def monomial(self, point):
X = self.parent().scheme()
fan = X.fan()
assert (point in fan.dual_lattice()), f'{point} must be a point in the M-lattice'
R = X.coordinate_ring()
return prod([(R.gen(i) ** ((point * fan.ray(i)) + self.coefficient(i))) for i in range(fan.nrays())])
def Kodaira_map(self, names='z'):
sections = self.sections_monomials()
if (not sections):
raise ValueError('the Kodaira map is not defined for divisors without sections')
src = self.parent().scheme()
from sage.schemes.projective.projective_space import ProjectiveSpace
ambient = ProjectiveSpace(src.base_ring(), (len(sections) - 1), names=names)
A = matrix(ZZ, [list(s.exponents()[0]) for s in sections]).transpose()
from sage.schemes.toric.ideal import ToricIdeal
IA = ToricIdeal(A, names=names)
dst = ambient.subscheme(IA)
homset = src.Hom(dst)
return homset(sections)
def _sheaf_complex(self, m):
fan = self.parent().scheme().fan()
ray_is_negative = [(((m * ray) + self.coefficient(i)) < 0) for (i, ray) in enumerate(fan.rays())]
def cone_is_negative(cone):
if cone.is_trivial():
return False
return all((ray_is_negative[i] for i in cone.ambient_ray_indices()))
negative_cones = [cone for cone in flatten(fan.cones()) if cone_is_negative(cone)]
return SimplicialComplex([c.ambient_ray_indices() for c in negative_cones])
def _sheaf_cohomology(self, cplx):
d = self.parent().scheme().dimension()
if (cplx.dimension() == (- 1)):
return vector(ZZ, ([1] + ([0] * d)))
HH = cplx.homology(base_ring=QQ, cohomology=True)
HH_list = ([0] * (d + 1))
for h in HH.items():
degree = (h[0] + 1)
cohomology_dim = h[1].dimension()
if ((degree > d) or (degree < 0)):
assert (cohomology_dim == 0)
continue
HH_list[degree] = cohomology_dim
return vector(ZZ, HH_list)
def _sheaf_cohomology_support(self):
X = self.parent().scheme()
fan = X.fan()
if (not X.is_complete()):
raise ValueError(('%s is not complete, its cohomology is not finite-dimensional' % X))
d = X.dimension()
chamber_vertices = []
for pindexlist in Combinations(range(fan.nrays()), d):
A = matrix(ZZ, [fan.ray(p) for p in pindexlist])
b = vector([self.coefficient(p) for p in pindexlist])
try:
chamber_vertices.append(A.solve_right((- b)))
except ValueError:
pass
return Polyhedron(vertices=chamber_vertices)
def cohomology(self, weight=None, deg=None, dim=False):
if (('_cohomology_vector' in self.__dict__) and (weight is None)):
HH = self._cohomology_vector
else:
X = self.parent().scheme()
M = X.fan().dual_lattice()
support = self._sheaf_cohomology_support()
if (weight is None):
m_list = [M(p) for p in support.integral_points()]
else:
m_list = [M(weight)]
HH = vector(ZZ, ([0] * (X.dimension() + 1)))
for m_point in m_list:
cplx = self._sheaf_complex(m_point)
HH += self._sheaf_cohomology(cplx)
if (weight is None):
self._cohomology_vector = HH
if dim:
if (deg is None):
return HH
else:
return HH[deg]
else:
from sage.modules.free_module import VectorSpace
vectorspaces = {k: VectorSpace(self.scheme().base_ring(), HH[k]) for k in range(len(HH))}
if (deg is None):
return vectorspaces
else:
return vectorspaces[deg]
def cohomology_support(self):
X = self.parent().scheme()
M = X.fan().dual_lattice()
support_hull = self._sheaf_cohomology_support()
support_hull = [M(p) for p in support_hull.integral_points()]
support = []
for m in support_hull:
cplx = self._sheaf_complex(m)
HH = self._sheaf_cohomology(cplx)
if (sum(HH) > 0):
support.append(m)
return tuple(support) |
def obtain_script_grounded_in_graph(lines_program, id_mapping, modified_script):
reverse_id_mapping = {}
for (object_script, id_sim) in id_mapping.items():
reverse_id_mapping[id_sim] = object_script
new_script = []
for script_line in modified_script:
script_line_str = '[{}]'.format(script_line.action.name)
if script_line.object():
try:
script_line_str += ' <{}> ({})'.format(*reverse_id_mapping[script_line.object().instance])
except:
print(id_mapping)
print(script_line.object().instance)
if script_line.subject():
script_line_str += ' <{}> ({})'.format(*reverse_id_mapping[script_line.subject().instance])
new_script.append(script_line_str)
lines_program = (lines_program[:4] + new_script)
return lines_program |
def set_map_fn(train_results: dict, task_settable_env: PcgrlEnv, env_ctx: EnvContext) -> TaskType:
if (not task_settable_env.evaluation_env):
return None
if task_settable_env.unwrapped._has_been_assigned_map:
map_idx = (task_settable_env.cur_map_idx + env_ctx['num_eval_envs'])
else:
map_idx = ((task_settable_env.cur_map_idx + (env_ctx.worker_index * env_ctx['num_envs_per_worker'])) + env_ctx.vector_index)
task_settable_env.unwrapped._has_been_assigned_map = True
map_idx = (map_idx % len(task_settable_env.unwrapped._prob.eval_maps))
print(f'Assigning map {map_idx} to environment {env_ctx.vector_index} of worker {env_ctx.worker_index}.')
return map_idx |
def write_prediction(pred_dict, batch, pred_y):
for (idx, (sample, py)) in enumerate(zip(batch, pred_y)):
(past, ground_truth, pose, vid, frame, pid, flipped, egomotion, scale, mag, size) = sample
(frame, pid) = (str(frame), str(pid))
err = np.linalg.norm((py - ground_truth), axis=1)[(- 1)]
front_cnt = sum([(1 if ((ps[11][0] - ps[8][0]) > 0) else 0) for ps in pose])
hip_dist = np.mean([np.abs((ps[(11, 0)] - ps[(8, 0)])) for ps in pose])
front_ratio = (front_cnt / len(pose))
if (hip_dist < 0.25):
traj_type = 2
elif (front_ratio > 0.75):
traj_type = 0
elif (front_ratio < 0.25):
traj_type = 1
else:
traj_type = 3
if (vid not in pred_dict):
pred_dict[vid] = {}
if (frame not in pred_dict[vid]):
pred_dict[vid][frame] = {}
result = [vid, frame, pid, flipped, py, None, None, err, traj_type]
result = list(map((lambda x: (x.tolist() if (type(x).__module__ == 'numpy') else x)), result))
pred_dict[vid][frame][pid] = result |
def test_var_get_position_no_statements(test_case_mock):
ref = vr.VariableReference(test_case_mock, int)
test_case_mock.statements = []
with pytest.raises(Exception):
ref.get_statement_position() |
def get_sub_dataset(pid2name, num_id=2000):
pids_all = sorted(pid2name.keys())
pids_sub = random.sample(pids_all, num_id)
data_sub = []
for pid in pids_sub:
lines = pid2name[pid]
data_sub += lines
return data_sub |
def typeset_term_syntax(term_class):
if ((len(term_class.arg_types) >= 1) and (not isinstance(term_class.arg_types[0], str))):
is_param = (len([arg for arg in term_class.arg_types[(- 1)] if arg.startswith('parameter')]) > 0)
is_vs = (len([arg for arg in term_class.arg_types[0] if (arg in ['virtual', 'state'])]) > 0)
arg_types_ = [list(k) for k in term_class.arg_types]
if (is_param and is_vs):
at0 = arg_types_[0]
at1 = arg_types_[(- 1)]
for k in range(len(at0)):
if ((at0[k] in ['virtual', 'state']) and at1[k].startswith('parameter')):
aux = at1[k].replace('parameter', 'param')
at0[k] = ((at0[k] + '/') + aux)
arg_types_ = arg_types_[:(- 1)]
arg_types = [', '.join([('``<%s>``' % arg) for arg in arg_type]) for arg_type in arg_types_]
text = '\n\n '.join(arg_types)
else:
text = ', '.join([('``<%s>``' % arg) for arg in term_class.arg_types])
return text |
class Issue19OneDataPointAtATime(ReBenchTestCase):
def setUp(self):
super(Issue19OneDataPointAtATime, self).setUp()
self._set_path(__file__) |
class BertTokenizer(Tokenizer):
def __init__(self, tokenizer):
super().__init__()
self._tokenizer = tokenizer
self._tokenizer.pad_token = '<pad>'
self._tokenizer.eos_token = '<eos>'
self._tokenizer.unk_token = '<unk>'
def encode(self, s: str) -> List[int]:
reduced_idx = []
for idx in self._tokenizer.encode(s):
try:
r_idx = (idx - BERT_FIRST_IDX)
assert (r_idx > 0)
reduced_idx.append(r_idx)
except AssertionError:
reduced_idx.append(self.unk_idx)
reduced_idx.append(self.eos_idx)
return reduced_idx
def decode(self, idxs: List[int], ignore_repeat: bool=False) -> str:
crop_idx = []
for (t, idx) in enumerate(idxs):
if (idx == self.eos_idx):
break
elif ((idx == self.pad_idx) or (ignore_repeat and (t > 0) and (idx == idxs[(t - 1)]))):
continue
else:
crop_idx.append((idx + BERT_FIRST_IDX))
return self._tokenizer.decode(crop_idx)
def load_from_file(cls, vocab_file: str):
from pytorch_transformers import BertTokenizer as bert_tokenizer
return cls(bert_tokenizer.from_pretrained(vocab_file))
def vocab_size(self) -> int:
return ((BERT_LAST_IDX - BERT_FIRST_IDX) + 1)
def token_type(self) -> str:
return 'bert' |
def grad(outputs, inputs, grad_outputs=None, persistent_outputs=[], bind_grad_output=False):
grad_outputs = Grad()(outputs, inputs, grad_outputs=grad_outputs, persistent_outputs=persistent_outputs, bind_grad_output=bind_grad_output)
return grad_outputs |
def base_axis_2_reshape_without_neg_1(x):
h = PF.convolution(x, 3, (3, 3), pad=(0, 0), name='c1', base_axis=2)
y = F.reshape(h, shape=(2, 18, 6))
return y |
def get_matrix_clusterers(cluster_count=3):
base_clusterers = [KMeans(cluster_count)]
for base_clusterer in base_clusterers:
(yield MatrixLabelSpaceClusterer(base_clusterer, False)) |
def _add_scores_to_sentences(sentences, scores):
for sentence in sentences:
if (sentence.token in scores):
sentence.score = scores[sentence.token]
else:
sentence.score = 0 |
def create_pipeline_configuration(DEBUG=False, batch_size=4):
config = {'batch_dim': 0, 'depth': 10000, 'basic_blocks': (T5Block, Linear, T5LayerNorm, CrossEntropyLoss, Dropout, StatelessEmbedding), 'model_inputs': {'attention_mask': {'shape': torch.Size([4, 1, 1, 512]), 'dtype': torch.float32, 'is_batched': True, 'used_by': [0, 1, 2, 3, 4, 5]}, 'decoder_attention_mask': {'shape': torch.Size([4, 1, 4, 4]), 'dtype': torch.float32, 'is_batched': True, 'used_by': [5, 6, 7]}, 'decoder_input_ids': {'shape': torch.Size([4, 4]), 'dtype': torch.int64, 'is_batched': True, 'used_by': [0, 5]}, 'input_ids': {'shape': torch.Size([4, 512]), 'dtype': torch.int64, 'is_batched': True, 'used_by': [0]}, 'inverted_encoder_attention_mask': {'shape': torch.Size([4, 1, 1, 512]), 'dtype': torch.float32, 'is_batched': True, 'used_by': [5, 6, 7]}, 'lm_labels': {'shape': torch.Size([4, 4]), 'dtype': torch.int64, 'is_batched': True, 'used_by': [7]}}, 'model_outputs': {'T5ForConditionalGeneration/CrossEntropyLoss[lm_loss]': {'shape': torch.Size([1]), 'dtype': torch.float32, 'is_batched': False, 'created_by': 7}}, 'stages': {0: {'stage_cls': Partition0, 'inputs': {'attention_mask': {'shape': torch.Size([4, 1, 1, 512]), 'dtype': torch.float32, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'decoder_input_ids': {'shape': torch.Size([4, 4]), 'dtype': torch.int64, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'input_ids': {'shape': torch.Size([4, 512]), 'dtype': torch.int64, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}}, 'outputs': {'T5ForConditionalGeneration/T5Stack[decoder]/Tensor::size_117': {'shape': torch.Size([2]), 'dtype': torch.Size, 'req_grad': False, 'is_batched': False, 'used_by': [5]}, 'T5ForConditionalGeneration/Parameter[shared_embed_weight]': {'shape': torch.Size([32100, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': False, 'used_by': [5]}, 'T5ForConditionalGeneration/T5Stack[encoder]/tuple::__getitem___22_1': {'shape': torch.Size([4, 32, 512, 512]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [1]}, 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[3]': {'shape': torch.Size([4, 512, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [1]}}, 'devices': [('cpu' if DEBUG else 'cuda:0')], 'stage_depth': 7}, 1: {'stage_cls': Partition1, 'inputs': {'attention_mask': {'shape': torch.Size([4, 1, 1, 512]), 'dtype': torch.float32, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'T5ForConditionalGeneration/T5Stack[encoder]/tuple::__getitem___22_1': {'shape': torch.Size([4, 32, 512, 512]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 0}, 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[3]': {'shape': torch.Size([4, 512, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 0}}, 'outputs': {'T5ForConditionalGeneration/T5Stack[encoder]/tuple::__getitem___22_2': {'shape': torch.Size([4, 32, 512, 512]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [2]}, 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[7]': {'shape': torch.Size([4, 512, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [2]}}, 'devices': [('cpu' if DEBUG else 'cuda:1')], 'stage_depth': 6}, 2: {'stage_cls': Partition2, 'inputs': {'attention_mask': {'shape': torch.Size([4, 1, 1, 512]), 'dtype': torch.float32, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'T5ForConditionalGeneration/T5Stack[encoder]/tuple::__getitem___22_2': {'shape': torch.Size([4, 32, 512, 512]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 1}, 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[7]': {'shape': torch.Size([4, 512, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 1}}, 'outputs': {'T5ForConditionalGeneration/T5Stack[encoder]/tuple::__getitem___22_3': {'shape': torch.Size([4, 32, 512, 512]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [3]}, 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[11]': {'shape': torch.Size([4, 512, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [3]}}, 'devices': [('cpu' if DEBUG else 'cuda:2')], 'stage_depth': 5}, 3: {'stage_cls': Partition3, 'inputs': {'attention_mask': {'shape': torch.Size([4, 1, 1, 512]), 'dtype': torch.float32, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'T5ForConditionalGeneration/T5Stack[encoder]/tuple::__getitem___22_3': {'shape': torch.Size([4, 32, 512, 512]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 2}, 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[11]': {'shape': torch.Size([4, 512, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 2}}, 'outputs': {'T5ForConditionalGeneration/T5Stack[encoder]/tuple::__getitem___22_4': {'shape': torch.Size([4, 32, 512, 512]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [4]}, 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[15]': {'shape': torch.Size([4, 512, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [4]}}, 'devices': [('cpu' if DEBUG else 'cuda:3')], 'stage_depth': 4}, 4: {'stage_cls': Partition4, 'inputs': {'attention_mask': {'shape': torch.Size([4, 1, 1, 512]), 'dtype': torch.float32, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'T5ForConditionalGeneration/T5Stack[encoder]/tuple::__getitem___22_4': {'shape': torch.Size([4, 32, 512, 512]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 3}, 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[15]': {'shape': torch.Size([4, 512, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 3}}, 'outputs': {'T5ForConditionalGeneration/T5Stack[encoder]/tuple::__getitem___22_5': {'shape': torch.Size([4, 32, 512, 512]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [5]}, 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[19]': {'shape': torch.Size([4, 512, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [5]}}, 'devices': [('cpu' if DEBUG else 'cuda:4')], 'stage_depth': 3}, 5: {'stage_cls': Partition5, 'inputs': {'attention_mask': {'shape': torch.Size([4, 1, 1, 512]), 'dtype': torch.float32, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'decoder_attention_mask': {'shape': torch.Size([4, 1, 4, 4]), 'dtype': torch.float32, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'decoder_input_ids': {'shape': torch.Size([4, 4]), 'dtype': torch.int64, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'inverted_encoder_attention_mask': {'shape': torch.Size([4, 1, 1, 512]), 'dtype': torch.float32, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'T5ForConditionalGeneration/T5Stack[decoder]/Tensor::size_117': {'shape': torch.Size([2]), 'dtype': torch.Size, 'req_grad': False, 'is_batched': False, 'created_by': 0}, 'T5ForConditionalGeneration/Parameter[shared_embed_weight]': {'shape': torch.Size([32100, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': False, 'created_by': 0}, 'T5ForConditionalGeneration/T5Stack[encoder]/tuple::__getitem___22_5': {'shape': torch.Size([4, 32, 512, 512]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 4}, 'T5ForConditionalGeneration/T5Stack[encoder]/T5Block[19]': {'shape': torch.Size([4, 512, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 4}}, 'outputs': {'T5ForConditionalGeneration/T5Stack[encoder]/Dropout[dropout]_6': {'shape': torch.Size([4, 512, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [6]}, 'T5ForConditionalGeneration/T5Stack[decoder]/tuple::__getitem___128': {'shape': torch.Size([4, 4, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [6]}, 'T5ForConditionalGeneration/T5Stack[decoder]/tuple::__getitem___130_6': {'shape': torch.Size([4, 32, 4, 4]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [6]}, 'T5ForConditionalGeneration/T5Stack[decoder]/tuple::__getitem___132_6': {'shape': torch.Size([4, 32, 4, 512]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [6]}}, 'devices': [('cpu' if DEBUG else 'cuda:5')], 'stage_depth': 2}, 6: {'stage_cls': Partition6, 'inputs': {'decoder_attention_mask': {'shape': torch.Size([4, 1, 4, 4]), 'dtype': torch.float32, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'inverted_encoder_attention_mask': {'shape': torch.Size([4, 1, 1, 512]), 'dtype': torch.float32, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'T5ForConditionalGeneration/T5Stack[encoder]/Dropout[dropout]_6': {'shape': torch.Size([4, 512, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 5}, 'T5ForConditionalGeneration/T5Stack[decoder]/tuple::__getitem___128': {'shape': torch.Size([4, 4, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 5}, 'T5ForConditionalGeneration/T5Stack[decoder]/tuple::__getitem___130_6': {'shape': torch.Size([4, 32, 4, 4]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 5}, 'T5ForConditionalGeneration/T5Stack[decoder]/tuple::__getitem___132_6': {'shape': torch.Size([4, 32, 4, 512]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 5}}, 'outputs': {'T5ForConditionalGeneration/T5Stack[encoder]/Dropout[dropout]_7': {'shape': torch.Size([4, 512, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [7]}, 'T5ForConditionalGeneration/T5Stack[decoder]/tuple::__getitem___130_7': {'shape': torch.Size([4, 32, 4, 4]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [7]}, 'T5ForConditionalGeneration/T5Stack[decoder]/tuple::__getitem___132_7': {'shape': torch.Size([4, 32, 4, 512]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [7]}, 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[12]': {'shape': torch.Size([4, 4, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'used_by': [7]}}, 'devices': [('cpu' if DEBUG else 'cuda:6')], 'stage_depth': 1}, 7: {'stage_cls': Partition7, 'inputs': {'decoder_attention_mask': {'shape': torch.Size([4, 1, 4, 4]), 'dtype': torch.float32, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'inverted_encoder_attention_mask': {'shape': torch.Size([4, 1, 1, 512]), 'dtype': torch.float32, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'lm_labels': {'shape': torch.Size([4, 4]), 'dtype': torch.int64, 'req_grad': False, 'is_batched': True, 'created_by': (- 1)}, 'T5ForConditionalGeneration/T5Stack[encoder]/Dropout[dropout]_7': {'shape': torch.Size([4, 512, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 6}, 'T5ForConditionalGeneration/T5Stack[decoder]/tuple::__getitem___130_7': {'shape': torch.Size([4, 32, 4, 4]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 6}, 'T5ForConditionalGeneration/T5Stack[decoder]/tuple::__getitem___132_7': {'shape': torch.Size([4, 32, 4, 512]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 6}, 'T5ForConditionalGeneration/T5Stack[decoder]/T5Block[12]': {'shape': torch.Size([4, 4, 1024]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': True, 'created_by': 6}}, 'outputs': {'T5ForConditionalGeneration/CrossEntropyLoss[lm_loss]': {'shape': torch.Size([1]), 'dtype': torch.float32, 'req_grad': True, 'is_batched': False, 'used_by': [(- 1)]}}, 'devices': [('cpu' if DEBUG else 'cuda:7')], 'stage_depth': 0}}}
batch_dim = config['batch_dim']
for d in chain(config['model_inputs'].values(), config['model_outputs'].values()):
if d['is_batched']:
shape = d['shape']
d['shape'] = torch.Size(((shape[:batch_dim] + (batch_size,)) + shape[(batch_dim + 1):]))
for s in config['stages'].values():
for d in chain(s['inputs'].values(), s['outputs'].values()):
if d['is_batched']:
shape = d['shape']
d['shape'] = torch.Size(((shape[:batch_dim] + (batch_size,)) + shape[(batch_dim + 1):]))
return config |
def create_loss():
bounds = (0.1, 3.0)
obs = zfit.Space('x', limits=bounds)
np.random.seed(0)
tau = (- 2.0)
beta = ((- 1) / tau)
bkg = np.random.exponential(beta, 300)
peak = np.random.normal(1.2, 0.1, 25)
data = np.concatenate((bkg, peak))
data = data[((data > bounds[0]) & (data < bounds[1]))]
N = len(data)
data = zfit.data.Data.from_numpy(obs=obs, array=data)
lambda_ = zfit.Parameter('lambda', (- 2.0), (- 4.0), (- 1.0))
Nsig = zfit.Parameter('Nsig', 20.0, (- 20.0), N)
Nbkg = zfit.Parameter('Nbkg', N, 0.0, (N * 1.1))
signal = zfit.pdf.Gauss(obs=obs, mu=1.2, sigma=0.1).create_extended(Nsig)
background = zfit.pdf.Exponential(obs=obs, lambda_=lambda_).create_extended(Nbkg)
tot_model = zfit.pdf.SumPDF([signal, background])
loss = ExtendedUnbinnedNLL(model=tot_model, data=data)
poigen = POI(Nsig, 0.0)
poieval = POIarray(Nsig, [0.0])
return (loss, (Nsig, poigen, poieval)) |
def test_non_list_index():
array = ak.Array([{'x': 10, 'y': 1.0}, {'x': 30, 'y': 20.0}, {'x': 40, 'y': 20.0}, {'x': 'hi', 'y': 20.0}])
assert (array[['x']].to_list() == [{'x': 10}, {'x': 30}, {'x': 40}, {'x': 'hi'}])
fields_ak = ak.Array(['x'])
assert (array[fields_ak].to_list() == [{'x': 10}, {'x': 30}, {'x': 40}, {'x': 'hi'}])
fields_np = np.array(['x'])
assert (array[fields_np].to_list() == [{'x': 10}, {'x': 30}, {'x': 40}, {'x': 'hi'}])
class SizedIterable():
def __len__(self):
return 1
def __iter__(self):
return iter(['y'])
fields_custom = SizedIterable()
assert (array[fields_custom].to_list() == [{'y': 1.0}, {'y': 20.0}, {'y': 20.0}, {'y': 20.0}])
fields_tuple = ('x',)
assert (array[fields_tuple].to_list() == [10, 30, 40, 'hi']) |
.parametrize('context, action_context, tau, err, description', invalid_input_of_linear_behavior_policy_logit)
def test_linear_behavior_policy_logit_using_invalid_input(context, action_context, tau, err, description):
if (description == ''):
with pytest.raises(err):
linear_behavior_policy_logit(context=context, action_context=action_context, tau=tau)
else:
with pytest.raises(err, match=f'{description}*'):
linear_behavior_policy_logit(context=context, action_context=action_context, tau=tau) |
def set_transformed_lm_head(model):
model.set_output_embeddings(MyLMHeadWithTransform(model.lm_head)) |
class ConvLSTMCell(nn.Module):
def __init__(self, input_channels, hidden_channels, kernel_size):
super(ConvLSTMCell, self).__init__()
self.input_channels = input_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.num_features = 4
self.padding = int(((kernel_size - 1) / 2))
self.W_i = nn.Conv2d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True)
self.W_f = nn.Conv2d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True)
self.W_o = nn.Conv2d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True)
self.W_c = nn.Conv2d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True)
self.reset_parameters()
def forward(self, inputs, c):
i_t = torch.sigmoid(self.W_i(inputs))
f_t = torch.sigmoid(self.W_f(inputs))
o_t = torch.sigmoid(self.W_o(inputs))
c_t = ((f_t * c) + (i_t * torch.tanh(self.W_c(inputs))))
h_t = (o_t * torch.tanh(c_t))
return (h_t, c_t)
def reset_parameters(self):
self.W_i.reset_parameters()
self.W_f.reset_parameters()
self.W_o.reset_parameters()
self.W_c.reset_parameters() |
def pioglitazone_mpo() -> GoalDirectedBenchmark:
smiles = 'O=C1NC(=O)SC1Cc3ccc(OCCc2ncc(cc2)CC)cc3'
pioglitazone = Chem.MolFromSmiles(smiles)
target_molw = mol_weight(pioglitazone)
similarity = TanimotoScoringFunction(smiles, fp_type='ECFP4', score_modifier=GaussianModifier(mu=0, sigma=0.1))
mw = RdkitScoringFunction(descriptor=mol_weight, score_modifier=GaussianModifier(mu=target_molw, sigma=10))
rb = RdkitScoringFunction(descriptor=num_rotatable_bonds, score_modifier=GaussianModifier(mu=2, sigma=0.5))
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name='Pioglitazone MPO', objective=GeometricMeanScoringFunction([similarity, mw, rb]), contribution_specification=specification) |
def eval_bool(x, default=False):
if (x is None):
return default
try:
return bool(eval(x))
except TypeError:
return default |
_numpy_output(non_zero=True, check_dtype=True)
def test_ufunc_tan_f(A: dace.float32[10]):
return np.tan(A) |
def register_Ns3LteSpectrumSignalParametersUlSrsFrame_methods(root_module, cls):
cls.add_method('Copy', 'ns3::Ptr< ns3::SpectrumSignalParameters >', [], is_virtual=True)
cls.add_constructor([])
cls.add_constructor([param('ns3::LteSpectrumSignalParametersUlSrsFrame const &', 'p')])
cls.add_instance_attribute('cellId', 'uint16_t', is_const=False)
return |
def create_anomaly_layout() -> html.Div:
return html.Div(id='anomaly_views', children=[html.Div(id='left-column-data', className='three columns', children=[create_control_panel()]), html.Div(className='nine columns', children=create_right_column())]) |
def warning_advice(self, *args, **kwargs):
no_advisory_warnings = os.getenv('TRANSFORMERS_NO_ADVISORY_WARNINGS', False)
if no_advisory_warnings:
return
self.warning(*args, **kwargs) |
_converter_regitstry('sPorD')
def sPorD_converter(context: 'SG2260Context', reg: sPorD_reg):
(n, c, h, w) = (reg[f'res0_{d}'] for d in 'nchw')
opd0 = dict(address=reg.opd0_addr, dtype=(reg.opt_opd0_prec, reg.opt_opd0_sign), shape=(n, c, reg.opd0_h, reg.opd0_w), layout=Layout.alignEU)
res0 = dict(address=reg.res0_addr, dtype=(reg.opt_res0_prec, reg.opt_opd0_sign), shape=(n, c, h, w), layout=Layout.alignEU)
opd1 = dict(address=reg.opd1_addr, dtype=(reg.opt_opd0_prec, reg.opt_opd1_sign), shape=(1, c, reg.opd1_h, reg.opd1_w), layout=Layout.compact, is_const=reg.opt_opd1_const)
opd2 = dict(address=reg.opd2_addr, dtype=(reg.opt_opd0_prec, reg.opt_opd2_sign), shape=(1, c, 1, 1), layout=Layout.compact, is_const=reg.opt_opd2_const)
opd3 = dict(address=reg.opd3_addr, dtype=(reg.opt_opd0_prec, reg.opt_opd0_sign), shape=(1, c, 1, 2), layout=Layout.compact, is_const=reg.opt_opd3_const)
opd5 = dict(address=0, dtype=DType.si32, shape=(1, c, 1, 2), layout=Layout.compact, is_const=reg.opt_opd5_const)
opds = []
if (reg.tsk_eu_typ in [0, 4, 3, 1]):
if (reg.tsk_eu_typ == 0):
opds = [opd0, opd1, opd2, opd3, opd5]
elif (reg.tsk_eu_typ == 1):
opds = [opd0, opd1, opd3, opd5]
else:
opds = [opd0, opd3, opd5]
else:
opd3['shape'] = (1, c, 1, 4)
opd3['dtype'] = DType.ui16
if (reg.tsk_eu_typ in [5, 6]):
opds = [opd0, opd1, opd2, opd3, opd5]
else:
opds = [opd0, opd2, opd3, opd5]
attr = dict(kernel=[reg.opd1_h, reg.opd1_w], stride=[reg.res_op_y_str, reg.res_op_x_str], in_zero=[reg.opd0_y_ins0, reg.opd0_x_ins0], ke_zero=[reg.opd1_y_ins0, reg.opd1_x_ins0], opt_kernel_rotate=bool(reg.opt_kernel_rotate), pad_mode=reg.pad_mode, pad=[reg[f'opd0_{x}_pad'] for x in ('up', 'dn', 'lf', 'rt')], round_mode=reg.opd2_n_str, shift=np.uint32([reg.res1_addr]).view(np.int8)[0])
if (not bool(reg.opt_rq)):
opds.remove(opd5)
elif bool(reg.opt_opd5_const):
opds.remove(opd5)
attr['multiplier'] = tgcr.getter(6)
attr['shift'] = int(np.binary_repr(tgcr.getter(32), width=32)[(- 8):(- 1)], 2)
attr['yzp'] = int(np.binary_repr(tgcr.getter(33), width=32)[(- 16):(- 1)], 2)
operands = [get_value(context, **x) for x in opds]
results = [get_value(context, **res0)]
return (results, attr, operands) |
def prepare_dvoice(data_folder, save_folder, train_csv_file=None, dev_csv_file=None, test_csv_file=None, accented_letters=False, language='fongbe', skip_prep=False):
if skip_prep:
return
if (train_csv_file is None):
train_csv_file = (data_folder + 'texts/train.csv')
else:
train_csv_file = train_csv_file
if (dev_csv_file is None):
dev_csv_file = (data_folder + 'texts/dev.csv')
else:
dev_csv_file = dev_csv_file
if (test_csv_file is None):
test_csv_file = (data_folder + 'texts/test.csv')
else:
test_csv_file = test_csv_file
if (not os.path.exists(save_folder)):
os.makedirs(save_folder)
ALFFA_LANGUAGES = ['amharic', 'fongbe', 'wolof']
if (language in ALFFA_LANGUAGES):
df = alffa_public_prepare(language, data_folder)
(train, dev, test) = train_validate_test_split(df)
train.to_csv(f'{data_folder}/train.csv', index=False, sep='\t')
dev.to_csv(f'{data_folder}/dev.csv', index=False, sep='\t')
test.to_csv(f'{data_folder}/test.csv', index=False, sep='\t')
if (language == 'swahili'):
df = swahili_prepare(data_folder)
(train, dev, test) = train_validate_test_split(df)
train.to_csv(f'{data_folder}/train.csv', index=False, sep='\t')
dev.to_csv(f'{data_folder}/dev.csv', index=False, sep='\t')
test.to_csv(f'{data_folder}/test.csv', index=False, sep='\t')
if (language == 'multilingual'):
ALFFA_LANGUAGES = ['amharic', 'wolof']
df_alffa = pd.DataFrame()
for lang in ALFFA_LANGUAGES:
data_folder2 = (data_folder + f'/ALFFA_PUBLIC/ASR/{lang.upper()}/data')
df_l = alffa_public_prepare(lang, data_folder2)
df_l['wav'] = df_l['wav'].map((lambda x: (f'ALFFA_PUBLIC/ASR/{lang.upper()}/data/' + x.replace(f'{data_folder}/', ''))))
df_alffa = pd.concat([df_alffa, df_l], ignore_index=True)
df_sw = swahili_prepare(data_folder)
train_darija = pd.read_csv(f'{data_folder}/DVOICE/darija/texts/train.csv', sep='\t')
dev_darija = pd.read_csv(f'{data_folder}/DVOICE/darija/texts/dev.csv', sep='\t')
test_darija = pd.read_csv(f'{data_folder}/DVOICE/darija/texts/test.csv', sep='\t')
df_dar = pd.concat([train_darija, dev_darija, test_darija], ignore_index=True)
df_dar['wav'] = df_dar['wav'].map((lambda x: ('DVOICE/darija/wavs/' + x)))
df = pd.concat([df_alffa, df_sw, df_dar], ignore_index=True)
(train, dev, test) = train_validate_test_split(df)
train.to_csv(f'{data_folder}/train.csv', index=False, sep='\t')
dev.to_csv(f'{data_folder}/dev.csv', index=False, sep='\t')
test.to_csv(f'{data_folder}/test.csv', index=False, sep='\t')
save_csv_train = (save_folder + '/train.csv')
save_csv_dev = (save_folder + '/dev.csv')
save_csv_test = (save_folder + '/test.csv')
if skip(save_csv_train, save_csv_dev, save_csv_test):
msg = ('%s already exists, skipping data preparation!' % save_csv_train)
logger.info(msg)
msg = ('%s already exists, skipping data preparation!' % save_csv_dev)
logger.info(msg)
msg = ('%s already exists, skipping data preparation!' % save_csv_test)
logger.info(msg)
return
check_dvoice_folders(data_folder, language)
if (train_csv_file is not None):
create_csv(train_csv_file, save_csv_train, data_folder, accented_letters, language)
if (dev_csv_file is not None):
create_csv(dev_csv_file, save_csv_dev, data_folder, accented_letters, language)
if (test_csv_file is not None):
create_csv(test_csv_file, save_csv_test, data_folder, accented_letters, language) |
def split_antimeridian_polygon(polygon: List[List[float]]) -> Tuple[(List, List)]:
(poly1, poly2) = ([], [])
split_loc = 0.0
has_split = False
for idx in range((len(polygon) - 1)):
first = polygon[idx]
second = polygon[(idx + 1)]
if (((abs(first[0]) < 180) and (abs(second[0]) > 180)) or ((abs(first[0]) > 180) and (abs(second[0]) < 180))):
split_loc = math.copysign(180.0, first[0])
new_lat = np.interp([split_loc], np.array([first[0], second[0]]), np.array([first[1], second[1]]))[0]
new_point = [split_loc, float(new_lat)]
poly1.append([split_loc, float(new_lat)])
poly2.append([split_loc, float(new_lat)])
elif (abs(first[0]) < 180):
poly1.append(first)
else:
poly2.append(first)
poly1.append(poly1[0])
poly2.append(poly2[0])
poly2_wrapped: List[List[float]] = []
for point in poly2:
if (split_loc > 0):
poly2_wrapped.append([((- 180) + (point[0] - 180)), point[1]])
else:
poly2_wrapped.append([(180 + (point[0] + 180)), point[1]])
mapping1 = shapely.geometry.mapping(shapely.geometry.Polygon(poly1))
mapping2 = shapely.geometry.mapping(shapely.geometry.polygon.orient(shapely.geometry.Polygon(poly2_wrapped), 1.0))
return (mapping1, mapping2) |
.parametrize('delta', [0.1, 0.2])
def test_ltt_different_delta(delta: float) -> None:
assert ltt_procedure(r_hat, alpha, delta, n) |
def test_case_6():
int_0 = 1187
queue_0 = module_0.Queue(int_0)
assert (f'{type(queue_0).__module__}.{type(queue_0).__qualname__}' == 'queue_example.Queue')
assert (queue_0.max == 1187)
assert (queue_0.head == 0)
assert (queue_0.tail == 0)
assert (queue_0.size == 0)
assert (f'{type(queue_0.data).__module__}.{type(queue_0.data).__qualname__}' == 'array.array')
assert (len(queue_0.data) == 1187)
bool_0 = queue_0.empty()
assert (bool_0 is False)
bool_1 = queue_0.enqueue(int_0)
assert (bool_1 is True)
assert (queue_0.tail == 1)
assert (queue_0.size == 1)
queue_1 = module_0.Queue(bool_1)
assert (f'{type(queue_1).__module__}.{type(queue_1).__qualname__}' == 'queue_example.Queue')
assert (queue_1.max is True)
assert (queue_1.head == 0)
assert (queue_1.tail == 0)
assert (queue_1.size == 0)
assert (f'{type(queue_1.data).__module__}.{type(queue_1.data).__qualname__}' == 'array.array')
assert (len(queue_1.data) == 1)
bool_2 = queue_1.full()
assert (bool_2 is False)
int_1 = 1441
bool_3 = queue_1.enqueue(int_1)
assert (bool_3 is True)
assert (queue_1.size == 1)
bool_4 = queue_1.full()
assert (bool_4 is True)
int_2 = 1080
queue_2 = module_0.Queue(int_2)
assert (queue_2.head == 0)
assert (queue_2.size == 0)
bool_5 = queue_1.full()
assert (bool_5 is True)
queue_3 = module_0.Queue(bool_5)
assert (f'{type(queue_3).__module__}.{type(queue_3).__qualname__}' == 'queue_example.Queue')
assert (queue_3.max is True)
assert (queue_3.head == 0)
assert (queue_3.tail == 0)
assert (queue_3.size == 0)
assert (f'{type(queue_3.data).__module__}.{type(queue_3.data).__qualname__}' == 'array.array')
assert (len(queue_3.data) == 1)
bool_6 = queue_3.empty()
assert (bool_6 is False)
bool_7 = queue_1.enqueue(bool_2)
bool_8 = queue_1.empty()
assert (bool_8 is True)
none_type_0 = queue_2.dequeue()
none_type_1 = queue_3.dequeue()
queue_4 = module_0.Queue(bool_4)
assert (queue_4.head == 0)
assert (queue_4.size == 0)
bool_9 = queue_4.empty()
assert (bool_9 is False)
int_3 = 2245
bool_10 = queue_2.empty()
assert (bool_10 is False)
bool_11 = queue_3.empty()
assert (bool_11 is False)
bool_12 = queue_0.full()
queue_5 = module_0.Queue(int_3)
assert (queue_5.head == 0)
assert (queue_5.size == 0)
int_4 = queue_0.dequeue()
assert (int_4 == 1187)
assert (queue_0.head == 1)
assert (queue_0.size == 0)
bool_13 = queue_3.empty()
assert (bool_13 is False)
none_type_2 = queue_4.dequeue()
int_5 = 481
queue_6 = module_0.Queue(int_5)
assert (queue_6.head == 0)
assert (queue_6.size == 0)
none_type_3 = queue_3.dequeue()
bool_14 = queue_6.enqueue(bool_4)
assert (queue_6.tail == 1)
assert (queue_6.size == 1)
none_type_4 = queue_3.dequeue()
bool_15 = queue_0.empty()
assert (bool_15 is False)
bool_16 = queue_3.full() |
class MobileCRNN(nn.Module):
def __init__(self, inputdim, outputdim, **kwargs):
super().__init__()
filters = ([1] + kwargs.get('filters', ([40] + ([160] * 5))))
kernels = kwargs.get('kernels', ([5] + ([3] * 5)))
paddings = kwargs.get('padding', ([2] + ([1] * 5)))
strides = kwargs.get('strides', ([2] + ([1] * 5)))
poolings = kwargs.get('pooling', (([(2, 4)] + ([(1, 2)] * 3)) + ([(1, 1)] * 2)))
features = nn.ModuleList()
for (h0, h1, kernel_size, padding, pooling, stride) in zip(filters, filters[1:], kernels, paddings, poolings, strides):
if (h0 == 1):
features.append(nn.Sequential(nn.BatchNorm2d(h0), nn.Conv2d(h0, h1, kernel_size=kernel_size, padding=padding, stride=stride), Swish()))
else:
features.append(InvertedResidual(h0, h1, 1, expand_ratio=6))
if (np.prod(pooling) > 1):
features.append(nn.MaxPool2d(pooling))
self.features = nn.Sequential(*features)
with torch.no_grad():
rnn_input_dim = self.features(torch.randn(1, 1, 500, inputdim)).shape
rnn_input_dim = (rnn_input_dim[1] * rnn_input_dim[(- 1)])
self.gru = BiGRU(inputdim=rnn_input_dim, outputdim=128)
self.temp_pool = parse_poolingfunction(kwargs.get('temppool', 'linear'), inputdim=int(256), outputdim=outputdim)
self.outputlayer = nn.Linear(256, outputdim)
def forward(self, x, mode='all'):
if (mode == 'all'):
x = x.unsqueeze(1)
x = self.features(x)
x = x.transpose(1, 2).contiguous().flatten((- 2))
(x, _) = self.gru(x)
decision_time = torch.sigmoid(self.outputlayer(x))
decision_time = torch.clamp(decision_time, min=1e-07, max=1.0)
decision = self.temp_pool(x, decision_time).squeeze(1)
decision = torch.clamp(decision, min=1e-07, max=1.0)
return (decision, decision_time)
if (mode == 'embed'):
x = x.unsqueeze(1)
x = self.features(x)
return x
elif (mode == 'cont'):
x = x.transpose(1, 2).contiguous().flatten((- 2))
(x, _) = self.gru(x)
decision_time = torch.sigmoid(self.outputlayer(x))
decision_time = torch.clamp(decision_time, min=1e-07, max=1.0)
decision = self.temp_pool(x, decision_time).squeeze(1)
decision = torch.clamp(decision, min=1e-07, max=1.0)
return (decision, decision_time) |
def build_transform(is_train, args):
mean = IMAGENET_DEFAULT_MEAN
std = IMAGENET_DEFAULT_STD
if is_train:
transform = create_transform(input_size=args.input_size, is_training=True, color_jitter=False, auto_augment='rand-m9-mstd0.5-inc1', interpolation='bicubic', mean=mean, std=std)
return transform
t = []
if (args.input_size <= 224):
crop_pct = (224 / 256)
else:
crop_pct = 1.0
size = int((args.input_size / crop_pct))
t.append(transforms.Resize(size, interpolation=PIL.Image.BICUBIC))
t.append(transforms.CenterCrop(args.input_size))
t.append(transforms.ToTensor())
t.append(transforms.Normalize(mean, std))
return transforms.Compose(t) |
def get_grand_parent_dir(f_name):
from pathlib import Path
if ('.' in f_name.split('/')[(- 1)]):
return get_grand_parent_dir(get_dir_of_file(f_name))
else:
return f'{Path(f_name).parent}/' |
def proceed(prompt, allowed_chars, error_prompt=None, default=None):
p = prompt
while True:
s = raw_input(p)
p = prompt
if ((not s) and default):
s = default
if s:
c = s[0].lower()
if (c in allowed_chars):
break
if error_prompt:
p = ('%c: %s\n%s' % (c, error_prompt, prompt))
return c |
def my_nested_rref_add(dst, rref_t1, t2):
return rpc.rpc_sync(dst, my_rref_add, args=(rref_t1, t2)) |
def check_train_pairs(raw_data, direction, all_test_data, mess_up_train={}):
(src, tgt) = direction.split('-')
path1 = f'{raw_data}/train.{src}-{tgt}.{src}'
path2 = f'{raw_data}/train.{src}-{tgt}.{tgt}'
if ((not os.path.exists(path1)) or (not os.path.exists(path2))):
return
with open(path1) as f1, open(path2) as f2:
for (src_line, tgt_line) in zip(f1, f2):
s = src_line.strip()
t = tgt_line.strip()
if (((s, t) in all_test_data) or ((t, s) in all_test_data)):
langs = mess_up_train.get((s, t), set())
langs.add(src)
langs.add(tgt)
mess_up_train[(s, t)] = langs |
def split_header(task, lines):
if (task in ['CoLA']):
return ([], lines)
elif (task in ['MNLI', 'MRPC', 'QNLI', 'QQP', 'RTE', 'SNLI', 'SST-2', 'STS-B', 'WNLI']):
return (lines[0:1], lines[1:])
else:
raise ValueError('Unknown GLUE task.') |
class SemistandardSkewTableaux(SkewTableaux):
def __classcall_private__(cls, p=None, mu=None, max_entry=None):
if (p is None):
if (mu is None):
return SemistandardSkewTableaux_all(max_entry)
raise ValueError('you must specify either a size or a shape')
if isinstance(p, (int, Integer)):
if (mu is None):
return SemistandardSkewTableaux_size(p, max_entry)
else:
return SemistandardSkewTableaux_size_weight(p, mu)
if (p in SkewPartitions()):
if (mu is None):
return SemistandardSkewTableaux_shape(p, max_entry)
else:
return SemistandardSkewTableaux_shape_weight(p, mu)
raise ValueError('invalid input')
def __contains__(self, x):
if (x not in SkewTableaux()):
return False
try:
x = self.element_class(self, x)
except Exception:
return False
return x.is_semistandard() |
_deepspeed
_torch_gpu
class TrainerIntegrationDeepSpeed(TrainerIntegrationDeepSpeedWithCustomConfig, TrainerIntegrationCommon):
def test_hf_ds_config_mismatch(self):
ds_config = self.get_config_dict(ZERO2)
per_device_train_batch_size = 2
ds_config['train_micro_batch_size_per_gpu'] = (per_device_train_batch_size + 2)
ds_config['train_batch_size'] = 1000
gradient_accumulation_steps = 2
ds_config['gradient_accumulation_steps'] = (gradient_accumulation_steps + 2)
max_grad_norm = 1.0
ds_config['gradient_clipping'] = (max_grad_norm + 0.1)
(adam_beta1, adam_beta2) = (0.9, 0.99)
ds_config['optimizer']['params']['betas'] = [(adam_beta1 - 0.1), (adam_beta2 - 0.1)]
fp16 = True
ds_config['fp16']['enabled'] = (not fp16)
keys = ['per_device_train_batch_size', 'train_batch_size', 'gradient_accumulation_steps', 'max_grad_norm', 'betas', 'fp16']
with mockenv_context(**self.dist_env_1_gpu):
trainer = get_regression_trainer(local_rank=0, fp16=fp16, deepspeed=ds_config, per_device_train_batch_size=per_device_train_batch_size, gradient_accumulation_steps=gradient_accumulation_steps, max_grad_norm=max_grad_norm, adam_beta1=adam_beta1, adam_beta2=adam_beta2)
with self.assertRaises(Exception) as context:
trainer.train()
for key in keys:
self.assertTrue((key in str(context.exception)), f'''{key} is not in the exception message:
{context.exception}''')
def test_hf_scheduler_hf_optimizer(self):
a = 0
with mockenv_context(**self.dist_env_1_gpu):
ds_config_zero2_dict = self.get_config_dict(ZERO2)
del ds_config_zero2_dict['optimizer']
del ds_config_zero2_dict['scheduler']
ds_config_zero2_dict['zero_optimization']['offload_optimizer']['device'] = 'none'
ds_config_zero2_dict['fp16']['initial_scale_power'] = 1
trainer = get_regression_trainer(a=a, local_rank=0, fp16=True, deepspeed=ds_config_zero2_dict)
trainer.train()
new_a = trainer.model.a.item()
self.assertNotEqual(new_a, a)
def test_ds_scheduler_hf_optimizer(self):
a = 0
with mockenv_context(**self.dist_env_1_gpu):
ds_config_zero2_dict = self.get_config_dict(ZERO2)
del ds_config_zero2_dict['optimizer']
ds_config_zero2_dict['zero_optimization']['offload_optimizer']['device'] = 'none'
ds_config_zero2_dict['fp16']['initial_scale_power'] = 1
trainer = get_regression_trainer(a=a, local_rank=0, fp16=True, deepspeed=ds_config_zero2_dict)
trainer.train()
new_a = trainer.model.a.item()
self.assertNotEqual(new_a, a)
def test_hf_scheduler_ds_optimizer(self):
a = 0
with mockenv_context(**self.dist_env_1_gpu):
ds_config_zero2_dict = self.get_config_dict(ZERO2)
del ds_config_zero2_dict['scheduler']
ds_config_zero2_dict['zero_optimization']['offload_optimizer']['device'] = 'none'
ds_config_zero2_dict['fp16']['initial_scale_power'] = 1
trainer = get_regression_trainer(local_rank=0, fp16=True, deepspeed=ds_config_zero2_dict)
trainer.train()
new_a = trainer.model.a.item()
self.assertNotEqual(new_a, a)
_deepspeed_aio
def test_stage3_nvme_offload(self):
with mockenv_context(**self.dist_env_1_gpu):
nvme_path = self.get_auto_remove_tmp_dir()
nvme_config = {'device': 'nvme', 'nvme_path': nvme_path}
ds_config_zero3_dict = self.get_config_dict(ZERO3)
ds_config_zero3_dict['zero_optimization']['offload_optimizer'] = nvme_config
ds_config_zero3_dict['zero_optimization']['offload_param'] = nvme_config
trainer = get_regression_trainer(local_rank=0, fp16=True, deepspeed=ds_config_zero3_dict)
with CaptureLogger(deepspeed_logger) as cl:
trainer.train()
self.assertIn('DeepSpeed info', cl.out, 'expected DeepSpeed logger output but got none')
_optuna
def test_hyperparameter_search(self):
with mockenv_context(**self.dist_env_1_gpu):
ds_config_zero3_dict = self.get_config_dict(ZERO3)
def model_init():
config = RegressionModelConfig(a=0, b=0, double_output=False)
model = RegressionPreTrainedModel(config)
return model
trainer = get_regression_trainer(local_rank=0, fp16=True, model_init=model_init, deepspeed=ds_config_zero3_dict)
n_trials = 3
with CaptureLogger(deepspeed_logger) as cl:
with CaptureStd() as cs:
trainer.hyperparameter_search(direction='maximize', n_trials=n_trials)
self.assertIn('DeepSpeed info', cl.out, 'expected DeepSpeed logger output but got none')
self.assertIn(f'Trial {(n_trials - 1)} finished with value', cs.err, 'expected hyperparameter_search output')
self.assertIn('Best is trial', cs.err, 'expected hyperparameter_search output')
(params, name_func=parameterized_custom_name_func)
def test_hf_optimizer_with_offload(self, stage, dtype):
ds_config_dict = self.get_config_dict(stage)
del ds_config_dict['optimizer']
ds_config_dict['zero_optimization']['offload_optimizer']['device'] = 'cpu'
ds_config_dict['zero_force_ds_cpu_optimizer'] = False
with mockenv_context(**self.dist_env_1_gpu):
kwargs = {'local_rank': 0, 'deepspeed': ds_config_dict}
kwargs[dtype] = True
trainer = get_regression_trainer(**kwargs)
with CaptureLogger(deepspeed_logger) as cl:
trainer.train()
self.assertIn('DeepSpeed info', cl.out, 'expected DeepSpeed logger output but got none')
(params, name_func=parameterized_custom_name_func)
def test_fake_notebook_no_launcher(self, stage, dtype):
with mockenv_context(**self.dist_env_1_gpu):
kwargs = {'local_rank': 0, 'deepspeed': self.get_config_dict(stage)}
kwargs[dtype] = True
trainer = get_regression_trainer(**kwargs)
with CaptureLogger(deepspeed_logger) as cl:
trainer.train()
self.assertIn('DeepSpeed info', cl.out, 'expected DeepSpeed logger output but got none')
(params, name_func=parameterized_custom_name_func)
def test_early_get_last_lr(self, stage, dtype):
with mockenv_context(**self.dist_env_1_gpu):
a = b = 0.0
kwargs = {'a': a, 'b': b, 'local_rank': 0, 'train_len': 8, 'deepspeed': self.get_config_dict(stage), 'per_device_train_batch_size': 8, 'logging_steps': 1}
kwargs[dtype] = True
trainer = get_regression_trainer(**kwargs)
trainer.train()
post_train_a = trainer.model.a.item()
if (((stage == ZERO3) and (dtype == FP16)) or (dtype == BF16)):
return
self.assertEqual(post_train_a, a)
(params, name_func=parameterized_custom_name_func)
def test_gradient_accumulation(self, stage, dtype):
train_len = 64
a = b = 0.0
kwargs = {'a': a, 'b': b, 'local_rank': 0, 'train_len': train_len, 'deepspeed': self.get_config_dict(stage)}
kwargs[dtype] = True
with mockenv_context(**self.dist_env_1_gpu):
no_grad_accum_trainer = get_regression_trainer(**kwargs, per_device_train_batch_size=16, gradient_accumulation_steps=1)
no_grad_accum_result = no_grad_accum_trainer.train()
no_grad_accum_loss = no_grad_accum_result.training_loss
no_grad_accum_a = no_grad_accum_trainer.model.a.item()
no_grad_accum_b = no_grad_accum_trainer.model.b.item()
self.assertNotEqual(no_grad_accum_a, a)
with mockenv_context(**self.dist_env_1_gpu):
yes_grad_accum_trainer = get_regression_trainer(**kwargs, per_device_train_batch_size=4, gradient_accumulation_steps=4)
yes_grad_accum_result = yes_grad_accum_trainer.train()
yes_grad_accum_loss = yes_grad_accum_result.training_loss
yes_grad_accum_a = yes_grad_accum_trainer.model.a.item()
yes_grad_accum_b = yes_grad_accum_trainer.model.b.item()
self.assertNotEqual(yes_grad_accum_a, a)
self.assertAlmostEqual(no_grad_accum_a, yes_grad_accum_a, places=5)
self.assertAlmostEqual(no_grad_accum_b, yes_grad_accum_b, places=5)
self.assertAlmostEqual(no_grad_accum_loss, yes_grad_accum_loss, places=2)
def check_saved_checkpoints_deepspeed(self, output_dir, freq, total, stage, dtype):
file_list = [WEIGHTS_NAME, 'training_args.bin', 'trainer_state.json', 'config.json']
if (stage == ZERO2):
ds_file_list = ['mp_rank_00_model_states.pt']
elif (stage == ZERO3):
ds_file_list = ['zero_pp_rank_0_mp_rank_00_model_states.pt']
else:
raise ValueError(f'unknown stage {stage}')
if (dtype == 'bf16'):
ds_file_list.append('bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt')
for step in range(freq, total, freq):
checkpoint = os.path.join(output_dir, f'checkpoint-{step}')
self.assertTrue(os.path.isdir(checkpoint), f'[{stage}] {checkpoint} dir is not found')
for filename in file_list:
path = os.path.join(checkpoint, filename)
self.assertTrue(os.path.isfile(path), f'[{stage}] {path} is not found')
ds_path = os.path.join(checkpoint, f'global_step{step}')
for filename in ds_file_list:
path = os.path.join(ds_path, filename)
self.assertTrue(os.path.isfile(path), f'[{stage}] {path} is not found')
(params, name_func=parameterized_custom_name_func)
def test_save_checkpoints(self, stage, dtype):
freq = 5
output_dir = self.get_auto_remove_tmp_dir()
ds_config_dict = self.get_config_dict(stage)
if (dtype == FP16):
ds_config_dict['fp16']['initial_scale_power'] = 1
if (stage == ZERO3):
ds_config_dict['zero_optimization']['stage3_gather_16bit_weights_on_model_save'] = True
with mockenv_context(**self.dist_env_1_gpu):
kwargs = {'output_dir': output_dir, 'save_steps': freq, 'deepspeed': ds_config_dict}
kwargs[dtype] = True
trainer = get_regression_trainer(**kwargs)
trainer.train()
total = int(((self.n_epochs * 64) / self.batch_size))
self.check_saved_checkpoints_deepspeed(output_dir, freq, total, stage, dtype)
(params, name_func=parameterized_custom_name_func)
def test_can_resume_training_errors(self, stage, dtype):
with mockenv_context(**self.dist_env_1_gpu):
ds_config_dict = self.get_config_dict(stage)
output_dir = self.get_auto_remove_tmp_dir()
kwargs = {'output_dir': output_dir, 'deepspeed': ds_config_dict}
kwargs[dtype] = True
trainer = get_regression_trainer(**kwargs)
with self.assertRaises(Exception) as context:
trainer.train(resume_from_checkpoint=True)
self.assertTrue(('No valid checkpoint found in output directory' in str(context.exception)), f'got exception: {context.exception}')
with self.assertRaises(Exception) as context:
checkpoint = os.path.join(output_dir, 'checkpoint-5')
trainer.train(resume_from_checkpoint=f'{checkpoint}-bogus')
self.assertTrue(("Can't find a valid checkpoint at" in str(context.exception)), f'got exception: {context.exception}')
(params, name_func=parameterized_custom_name_func)
def test_can_resume_training_normal(self, stage, dtype):
output_dir = self.get_auto_remove_tmp_dir('./xxx', after=False)
ds_config_dict = self.get_config_dict(stage)
if (dtype == FP16):
ds_config_dict['fp16']['initial_scale_power'] = 1
if (stage == ZERO3):
ds_config_dict['zero_optimization']['stage3_gather_16bit_weights_on_model_save'] = True
kwargs = {'output_dir': output_dir, 'train_len': 128, 'save_steps': 5, 'learning_rate': 0.1, 'deepspeed': ds_config_dict}
kwargs[dtype] = True
with mockenv_context(**self.dist_env_1_gpu):
trainer = get_regression_trainer(**kwargs)
trainer.train()
(a, b) = (trainer.model.a.item(), trainer.model.b.item())
state = dataclasses.asdict(trainer.state)
checkpoint = os.path.join(output_dir, 'checkpoint-5')
trainer = get_regression_trainer(**kwargs)
trainer.train(resume_from_checkpoint=checkpoint)
(a1, b1) = (trainer.model.a.item(), trainer.model.b.item())
state1 = dataclasses.asdict(trainer.state)
self.assertEqual(a, a1)
self.assertEqual(b, b1)
self.check_trainer_state_are_the_same(state, state1)
checkpoint = os.path.join(output_dir, 'checkpoint-15')
trainer = get_regression_trainer(**kwargs)
trainer.train(resume_from_checkpoint=checkpoint)
(a1, b1) = (trainer.model.a.item(), trainer.model.b.item())
state1 = dataclasses.asdict(trainer.state)
self.assertEqual(a, a1)
self.assertEqual(b, b1)
self.check_trainer_state_are_the_same(state, state1)
(params, name_func=parameterized_custom_name_func)
def test_load_state_dict_from_zero_checkpoint(self, stage, dtype):
output_dir = self.get_auto_remove_tmp_dir()
ds_config_dict = self.get_config_dict(stage)
kwargs = {'output_dir': output_dir, 'train_len': 4, 'per_device_train_batch_size': 4, 'num_train_epochs': 1, 'save_strategy': 'steps', 'save_steps': 1, 'learning_rate': 0.1, 'deepspeed': ds_config_dict}
kwargs[dtype] = True
with mockenv_context(**self.dist_env_1_gpu):
trainer = get_regression_trainer(**kwargs)
trainer.train()
(a, b) = (trainer.model.a.item(), trainer.model.b.item())
state = dataclasses.asdict(trainer.state)
checkpoint_dir = get_last_checkpoint(output_dir)
model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
(a1, b1) = (model.a.item(), model.b.item())
state1 = dataclasses.asdict(trainer.state)
self.assertEqual(a, a1)
self.assertEqual(b, b1)
self.check_trainer_state_are_the_same(state, state1)
def test_config_object(self):
output_dir = self.get_auto_remove_tmp_dir()
kwargs = {'output_dir': output_dir, 'train_len': 8, 'fp16': True}
ds_config_zero3_dict = self.get_config_dict(ZERO3)
ds_config_zero2_dict = self.get_config_dict(ZERO2)
with mockenv_context(**self.dist_env_1_gpu):
trainer = get_regression_trainer(deepspeed=ds_config_zero3_dict, **kwargs)
self.assertTrue(is_deepspeed_zero3_enabled())
trainer = get_regression_trainer(deepspeed=ds_config_zero3_dict, **kwargs)
trainer.train()
self.assertTrue(is_deepspeed_zero3_enabled())
trainer = get_regression_trainer(deepspeed=ds_config_zero2_dict, **kwargs)
self.assertFalse(is_deepspeed_zero3_enabled())
config = deepspeed_config()
self.assertTrue(bool(config), 'Deepspeed config should be accessible')
del trainer
config = deepspeed_config()
self.assertFalse(is_deepspeed_zero3_enabled())
self.assertFalse(bool(config), 'Deepspeed config should not be accessible')
(params, name_func=parameterized_custom_name_func)
def test_load_best_model(self, stage, dtype):
from transformers import T5ForConditionalGeneration, T5Tokenizer, Trainer
output_dir = self.get_auto_remove_tmp_dir()
ds_config_dict = self.get_config_dict(stage)
del ds_config_dict['optimizer']
del ds_config_dict['scheduler']
ds_config_dict['zero_force_ds_cpu_optimizer'] = False
ds_config_dict['zero_optimization']['stage3_gather_16bit_weights_on_model_save'] = True
with mockenv_context(**self.dist_env_1_gpu):
args_dict = {'per_gpu_train_batch_size': 1, 'per_gpu_eval_batch_size': 1, 'gradient_accumulation_steps': 1, 'learning_rate': 0.0001, 'num_train_epochs': 1, 'do_train': True, 'do_eval': True, 'optim': 'adafactor', 'evaluation_strategy': 'steps', 'eval_steps': 1, 'save_strategy': 'steps', 'save_steps': 1, 'load_best_model_at_end': True, 'max_steps': 1, 'deepspeed': ds_config_dict, 'report_to': 'none'}
training_args = TrainingArguments(output_dir, **args_dict)
tokenizer = T5Tokenizer.from_pretrained(T5_TINY)
model = T5ForConditionalGeneration.from_pretrained(T5_TINY)
def _add_eos_to_examples(example):
example['input_text'] = f"question: {example['question']} context: {example['context']}"
example['target_text'] = (example['answers']['text'][0] if (len(example['answers']['text']) > 0) else '')
return example
def _convert_to_features(example_batch):
input_encodings = tokenizer.batch_encode_plus(example_batch['input_text'], pad_to_max_length=True, max_length=512, truncation=True)
target_encodings = tokenizer.batch_encode_plus(example_batch['target_text'], pad_to_max_length=True, max_length=16, truncation=True)
encodings = {'input_ids': input_encodings['input_ids'], 'attention_mask': input_encodings['attention_mask'], 'labels': target_encodings['input_ids']}
return encodings
def get_dataset():
data_file = str((self.tests_dir / 'fixtures/tests_samples/SQUAD/sample.json'))
data_files = {'train': data_file, 'validation': data_file}
raw_datasets = datasets.load_dataset('json', data_files=data_files, field='data')
train_dataset = raw_datasets['train'].map(_add_eos_to_examples).map(_convert_to_features, batched=True)
valid_dataset = deepcopy(train_dataset)
return (train_dataset, valid_dataset)
(train_dataset, eval_dataset) = get_dataset()
trainer = Trainer(model=model, tokenizer=tokenizer, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset)
trainer.train()
trainer.evaluate() |
def read_index(path):
data = []
for line in open(path, 'r', encoding='UTF-8'):
line = line.replace('\n', '')
data.append(line)
return data |
def set_pp_option(k, v):
if (k == 'html_mode'):
if v:
set_html_mode(True)
else:
set_html_mode(False)
return True
if (k == 'fpa_pretty'):
if v:
set_fpa_pretty(True)
else:
set_fpa_pretty(False)
return True
val = getattr(_PP, k, None)
if (val is not None):
_z3_assert(isinstance(v, type(val)), 'Invalid pretty print option value')
setattr(_PP, k, v)
return True
val = getattr(_Formatter, k, None)
if (val is not None):
_z3_assert(isinstance(v, type(val)), 'Invalid pretty print option value')
setattr(_Formatter, k, v)
return True
return False |
def search_concept_net(keyword):
keyword = keyword.lower().replace(' ', '_')
neighbors = []
for relation in extracted_concept_net:
if ((relation in BANNED_RELATIONS) or (keyword not in extracted_concept_net[relation])):
continue
for (h0, h1, weight) in extracted_concept_net[relation][keyword]:
if (h0 == h1 == keyword):
continue
neighbors.append({'entity': (h0 if (h1 == keyword) else h1), 'reasoning': f'ConceptNet: [[{h0}]] {relation} [[{h1}]]', 'relation_weight': weight})
for i in range(len(neighbors)):
neighbors[i]['entity'] = neighbors[i]['entity'].replace('_', ' ')
return neighbors |
class PhysicalQuantity(float):
def __new__(cls, value):
return float.__new__(cls, value)
def __add__(self, x):
assert_(isinstance(x, PhysicalQuantity))
return PhysicalQuantity((float(x) + float(self)))
__radd__ = __add__
def __sub__(self, x):
assert_(isinstance(x, PhysicalQuantity))
return PhysicalQuantity((float(self) - float(x)))
def __rsub__(self, x):
assert_(isinstance(x, PhysicalQuantity))
return PhysicalQuantity((float(x) - float(self)))
def __mul__(self, x):
return PhysicalQuantity((float(x) * float(self)))
__rmul__ = __mul__
def __div__(self, x):
return PhysicalQuantity((float(self) / float(x)))
def __rdiv__(self, x):
return PhysicalQuantity((float(x) / float(self))) |
class CodeReference(Reference):
test_cases: Optional[Dict] = None
def __init__(self, test_cases=None, **kw):
self.test_cases = test_cases
super(CodeReference, self).__init__(**kw) |
class CymruIpOriginService(Service):
__records: List[str]
__dns: DomainNameService
def __init__(self):
super().__init__()
self.__records = []
self.addDependency('DomainNameService', True, True)
self.addDependency('Base', False, False)
def _createServer(self) -> Server:
return CymruIpOriginServer()
def getName(self) -> str:
return 'CymruIpOriginService'
def addMapping(self, prefix: str, asn: int) -> CymruIpOriginService:
[pfx, cidr] = prefix.split('/')
cidr = int(cidr)
assert (cidr <= 24), 'Invalid prefix.'
prefix = IPv4Network(prefix)
sub_cidr = 24
num_8s = 3
if (cidr >= 0):
sub_cidr = 8
num_8s = 1
if (cidr >= 9):
sub_cidr = 16
num_8s = 2
if (cidr >= 17):
sub_cidr = 24
num_8s = 3
for net in prefix.subnets(new_prefix=sub_cidr):
record = '*.'
record += '.'.join(reversed(str(net).split('.')[0:3]))
record += '.origin.asn TXT "{} | {} | ZZ | SEED | 0000-00-00"'.format(asn, net)
self.__records.append(record)
return self
def getRecords(self) -> List[str]:
return self.__records
def addRecord(self, record: str) -> CymruIpOriginService:
self.__records.append(record)
return self
def _doInstall(self, node: Node, server: Server):
assert False, 'CymruIpOriginService is not a real service and should not be installed this way. Please install a DomainNameService on the node and host the zone "cymru.com." yourself.'
def configure(self, emulator: Emulator):
reg = emulator.getRegistry()
mappings: List[Tuple[(str, str)]] = []
self._log('Collecting all networks in the simulation...')
for regobj in reg.getAll().items():
[(asn, type, name), obj] = regobj
if (type != 'net'):
continue
net: Network = obj
if (asn == 'ix'):
asn = name.replace('ix', '')
asn_val = 0
try:
asn_val = int(asn)
except ValueError:
asn_val = 0
mappings.append((net.getPrefix(), asn_val))
for mapping in mappings:
(prefix, asn) = mapping
self.addMapping(str(prefix), asn)
self._log('Creating "cymru.com." zone...')
dns: DomainNameService = reg.get('seedemu', 'layer', 'DomainNameService')
zone = dns.getZone('cymru.com.')
self.__dns = dns
self._log('Adding mappings...')
for record in self.__records:
zone.addRecord(record)
return super().configure(emulator)
def print(self, indent: int) -> str:
out = (' ' * indent)
out += 'CymruIpOriginService\n'
return out |
class BasicDecoder(nn.Module):
def __init__(self, insize, outsize, kernel=3, init_stride=1, pad=1, last_layer=False):
super().__init__()
self.last_layer = last_layer
self.baseconv = nn.Sequential(nn.Conv3d(insize, insize, kernel_size=kernel, stride=init_stride, padding=pad), nn.InstanceNorm3d(insize), nn.LeakyReLU(inplace=True))
self.conv1x1 = nn.Conv3d(insize, outsize, kernel_size=1, stride=1, padding=0)
self.upconv = nn.Sequential(nn.InstanceNorm3d(outsize), nn.LeakyReLU(inplace=True), nn.Upsample(scale_factor=2, mode='nearest'), nn.Conv3d(outsize, outsize, kernel_size=3, stride=1, padding=1), nn.InstanceNorm3d(outsize), nn.LeakyReLU(inplace=True))
self.upconv_lastlayer = nn.Sequential(nn.Conv3d(insize, outsize, kernel_size=kernel, stride=init_stride, padding=pad), nn.InstanceNorm3d(outsize), nn.LeakyReLU(inplace=True))
for m in self.children():
if isinstance(m, nn.Conv3d):
init_weights(m, init_type='kaiming')
def forward(self, x):
if (self.last_layer == False):
out = self.baseconv(x)
out = self.conv1x1(out)
out = self.upconv(out)
return out
else:
return self.upconv_lastlayer(x) |
def standardize(x, axis, eps):
x = (x - jnp.mean(x, axis=axis, keepdims=True))
x = (x / jnp.sqrt((jnp.mean(jnp.square(x), axis=axis, keepdims=True) + eps)))
return x |
def generate_parameter_list(node_args, node_kwargs, ready_expressions, should_inject_device=False, string=True):
has_device_arg = any(((a.value_type is torch.device) for a in node_args))
has_device_arg |= any(((a.value_type is torch.device) for a in node_kwargs.keys()))
args = [ready_expressions[a] for a in node_args]
kwargs = []
for (a, kws) in node_kwargs.items():
for k in kws:
kwargs.append(f'{k}={ready_expressions[a]}')
if (should_inject_device and (not has_device_arg)):
kwargs.append('device=self.device')
if string:
return ', '.join((args + kwargs))
return (args + kwargs) |
def start_master(port: int=None, config: Config=None, config_path: str=None, block: bool=False, watchdog: bool=True, no_workers_timeout: float=30, new_job_retries_limit: int=5):
config = (config or Config(config_path))
port = (port or config.master_port)
db = bindings.Database(config.storage_config, config.db_path, ((config.master_address + ':') + port))
result = bindings.start_master(db, port, SCRIPT_DIR, watchdog, no_workers_timeout, new_job_retries_limit)
if (not result.success()):
raise ScannerException('Failed to start master: {}'.format(result.msg()))
if block:
bindings.wait_for_server_shutdown(db)
return db |
def sym_norm(adj):
if isinstance(adj, torch_sparse.SparseTensor):
adj_t = gcn_norm(adj, add_self_loops=True)
return adj_t |
class BootstrapFewShotWithRandomSearch(Teleprompter):
def __init__(self, metric, teacher_settings={}, max_bootstrapped_demos=4, max_labeled_demos=16, max_rounds=1, num_candidate_programs=16, num_threads=6, stop_at_score=None):
self.metric = metric
self.teacher_settings = teacher_settings
self.max_rounds = max_rounds
self.num_threads = num_threads
self.stop_at_score = stop_at_score
self.min_num_samples = 1
self.max_num_samples = max_bootstrapped_demos
self.num_candidate_sets = num_candidate_programs
self.max_labeled_demos = max_labeled_demos
print('Going to sample between', self.min_num_samples, 'and', self.max_num_samples, 'traces per predictor.')
print('Will attempt to train', self.num_candidate_sets, 'candidate sets.')
def compile(self, student, *, teacher=None, trainset, valset=None, restrict=None):
self.trainset = trainset
self.valset = (valset or trainset)
scores = []
all_subscores = []
score_data = []
for seed in range((- 3), self.num_candidate_sets):
if ((restrict is not None) and (seed not in restrict)):
print(seed, restrict)
continue
trainset2 = list(self.trainset)
if (seed == (- 3)):
program2 = student.reset_copy()
elif (seed == (- 2)):
teleprompter = LabeledFewShot(k=self.max_labeled_demos)
program2 = teleprompter.compile(student, trainset=trainset2)
elif (seed == (- 1)):
program = BootstrapFewShot(metric=self.metric, max_bootstrapped_demos=self.max_num_samples, max_labeled_demos=self.max_labeled_demos, teacher_settings=self.teacher_settings, max_rounds=self.max_rounds)
program2 = program.compile(student, teacher=teacher, trainset=trainset2)
else:
assert (seed >= 0), seed
random.Random(seed).shuffle(trainset2)
size = random.Random(seed).randint(self.min_num_samples, self.max_num_samples)
teleprompter = BootstrapFewShot(metric=self.metric, max_bootstrapped_demos=size, max_labeled_demos=self.max_labeled_demos, teacher_settings=self.teacher_settings, max_rounds=self.max_rounds)
program2 = teleprompter.compile(student, teacher=teacher, trainset=trainset2)
evaluate = Evaluate(devset=self.valset, metric=self.metric, num_threads=self.num_threads, display_table=False, display_progress=True)
(score, subscores) = evaluate(program2, return_all_scores=True)
all_subscores.append(subscores)
print('Score:', score, 'for set:', [len(predictor.demos) for predictor in program2.predictors()])
if ((len(scores) == 0) or (score > max(scores))):
print('New best score:', score, 'for seed', seed)
best_program = program2
scores.append(score)
print(f'Scores so far: {scores}')
print('Best score:', max(scores))
score_data.append((score, subscores, seed, program2))
if (len(score_data) > 2):
for k in [1, 2, 3, 5, 8, 9999]:
top_3_scores = sorted(score_data, key=(lambda x: x[0]), reverse=True)[:k]
transposed_subscores = zip(*[subscores for (_, subscores, *_) in top_3_scores if subscores])
avg_of_max_per_entry = (sum((max(entry) for entry in transposed_subscores)) / len(top_3_scores[0][1]))
print(f'Average of max per entry across top {k} scores: {avg_of_max_per_entry}')
if ((self.stop_at_score is not None) and (score >= self.stop_at_score)):
print(f'Stopping early because score {score} is >= stop_at_score {self.stop_at_score}')
break
best_program.candidate_programs = score_data
best_program.candidate_programs = sorted(best_program.candidate_programs, key=(lambda x: x[0]), reverse=True)
print(len(best_program.candidate_programs), 'candidate programs found.')
return best_program |
def test_unknown_reference():
config = {'param1': Ref('param2')}
pipeline = Pipeline(config)
with pytest.raises(KeyError):
pipeline.execute()
config = {'mydict': {'param1': Ref('mydict.param2')}}
pipeline = Pipeline(config)
with pytest.raises(KeyError):
pipeline.execute()
config = {'tables': {'mytable': {'mycolumn': [0, 1, 2]}}, 'myvalue': Ref('mytable.myothercolumn')}
pipeline = Pipeline(config)
with pytest.raises(KeyError):
pipeline.execute() |
def wrap_logical_op_with_cast_to_and_from(to_type):
def decorator(fn):
def wrap_with_cast(g, input, other):
to_cast_func = globals()['_cast_{}'.format(to_type)]
from_cast_func = wrap_logical_op_with_cast_to(input.type().scalarType())(fn)
return from_cast_func(g, to_cast_func(g, input, False), to_cast_func(g, other, False))
return wrap_with_cast
return decorator |
def create_linear_automl(task: Task, n_folds: int=5, timeout: Optional[None]=None, n_reader_jobs: int=1, cpu_limit: int=4, random_state: int=42):
torch.set_num_threads(cpu_limit)
reader = PandasToPandasReader(task, cv=n_folds, random_state=random_state, n_jobs=n_reader_jobs)
pipe = LinearFeatures()
model = LinearLBFGS()
pipeline = MLPipeline([model], pre_selection=None, features_pipeline=pipe, post_selection=None)
automl = AutoML(reader, [[pipeline]], skip_conn=False)
return automl |
def register_methods(root_module):
register_Ns3Address_methods(root_module, root_module['ns3::Address'])
register_Ns3AllocationRetentionPriority_methods(root_module, root_module['ns3::AllocationRetentionPriority'])
register_Ns3Angles_methods(root_module, root_module['ns3::Angles'])
register_Ns3AttributeConstructionList_methods(root_module, root_module['ns3::AttributeConstructionList'])
register_Ns3AttributeConstructionListItem_methods(root_module, root_module['ns3::AttributeConstructionList::Item'])
register_Ns3BandInfo_methods(root_module, root_module['ns3::BandInfo'])
register_Ns3Box_methods(root_module, root_module['ns3::Box'])
register_Ns3Buffer_methods(root_module, root_module['ns3::Buffer'])
register_Ns3BufferIterator_methods(root_module, root_module['ns3::Buffer::Iterator'])
register_Ns3BufferSizeLevelBsr_methods(root_module, root_module['ns3::BufferSizeLevelBsr'])
register_Ns3BuildBroadcastListElement_s_methods(root_module, root_module['ns3::BuildBroadcastListElement_s'])
register_Ns3BuildDataListElement_s_methods(root_module, root_module['ns3::BuildDataListElement_s'])
register_Ns3BuildRarListElement_s_methods(root_module, root_module['ns3::BuildRarListElement_s'])
register_Ns3BwPart_s_methods(root_module, root_module['ns3::BwPart_s'])
register_Ns3ByteTagIterator_methods(root_module, root_module['ns3::ByteTagIterator'])
register_Ns3ByteTagIteratorItem_methods(root_module, root_module['ns3::ByteTagIterator::Item'])
register_Ns3ByteTagList_methods(root_module, root_module['ns3::ByteTagList'])
register_Ns3ByteTagListIterator_methods(root_module, root_module['ns3::ByteTagList::Iterator'])
register_Ns3ByteTagListIteratorItem_methods(root_module, root_module['ns3::ByteTagList::Iterator::Item'])
register_Ns3CallbackBase_methods(root_module, root_module['ns3::CallbackBase'])
register_Ns3ConstantVelocityHelper_methods(root_module, root_module['ns3::ConstantVelocityHelper'])
register_Ns3CqiConfig_s_methods(root_module, root_module['ns3::CqiConfig_s'])
register_Ns3CqiListElement_s_methods(root_module, root_module['ns3::CqiListElement_s'])
register_Ns3DataOutputCallback_methods(root_module, root_module['ns3::DataOutputCallback'])
register_Ns3DataRate_methods(root_module, root_module['ns3::DataRate'])
register_Ns3DciInfoElement_methods(root_module, root_module['ns3::DciInfoElement'])
register_Ns3DciInfoElementTdma_methods(root_module, root_module['ns3::DciInfoElementTdma'])
register_Ns3DefaultDeleter__Ns3AttributeAccessor_methods(root_module, root_module['ns3::DefaultDeleter< ns3::AttributeAccessor >'])
register_Ns3DefaultDeleter__Ns3AttributeChecker_methods(root_module, root_module['ns3::DefaultDeleter< ns3::AttributeChecker >'])
register_Ns3DefaultDeleter__Ns3AttributeValue_methods(root_module, root_module['ns3::DefaultDeleter< ns3::AttributeValue >'])
register_Ns3DefaultDeleter__Ns3BeamformingParams_methods(root_module, root_module['ns3::DefaultDeleter< ns3::BeamformingParams >'])
register_Ns3DefaultDeleter__Ns3CallbackImplBase_methods(root_module, root_module['ns3::DefaultDeleter< ns3::CallbackImplBase >'])
register_Ns3DefaultDeleter__Ns3ChannelParams_methods(root_module, root_module['ns3::DefaultDeleter< ns3::ChannelParams >'])
register_Ns3DefaultDeleter__Ns3EpcTft_methods(root_module, root_module['ns3::DefaultDeleter< ns3::EpcTft >'])
register_Ns3DefaultDeleter__Ns3EventImpl_methods(root_module, root_module['ns3::DefaultDeleter< ns3::EventImpl >'])
register_Ns3DefaultDeleter__Ns3HashImplementation_methods(root_module, root_module['ns3::DefaultDeleter< ns3::Hash::Implementation >'])
register_Ns3DefaultDeleter__Ns3LteControlMessage_methods(root_module, root_module['ns3::DefaultDeleter< ns3::LteControlMessage >'])
register_Ns3DefaultDeleter__Ns3LteHarqPhy_methods(root_module, root_module['ns3::DefaultDeleter< ns3::LteHarqPhy >'])
register_Ns3DefaultDeleter__Ns3MmWaveControlMessage_methods(root_module, root_module['ns3::DefaultDeleter< ns3::MmWaveControlMessage >'])
register_Ns3DefaultDeleter__Ns3MmWaveHarqPhy_methods(root_module, root_module['ns3::DefaultDeleter< ns3::MmWaveHarqPhy >'])
register_Ns3DefaultDeleter__Ns3NixVector_methods(root_module, root_module['ns3::DefaultDeleter< ns3::NixVector >'])
register_Ns3DefaultDeleter__Ns3Packet_methods(root_module, root_module['ns3::DefaultDeleter< ns3::Packet >'])
register_Ns3DefaultDeleter__Ns3Params3gpp_methods(root_module, root_module['ns3::DefaultDeleter< ns3::Params3gpp >'])
register_Ns3DefaultDeleter__Ns3QueueItem_methods(root_module, root_module['ns3::DefaultDeleter< ns3::QueueItem >'])
register_Ns3DefaultDeleter__Ns3SpectrumModel_methods(root_module, root_module['ns3::DefaultDeleter< ns3::SpectrumModel >'])
register_Ns3DefaultDeleter__Ns3SpectrumValue_methods(root_module, root_module['ns3::DefaultDeleter< ns3::SpectrumValue >'])
register_Ns3DefaultDeleter__Ns3TraceParams_methods(root_module, root_module['ns3::DefaultDeleter< ns3::TraceParams >'])
register_Ns3DefaultDeleter__Ns3TraceSourceAccessor_methods(root_module, root_module['ns3::DefaultDeleter< ns3::TraceSourceAccessor >'])
register_Ns3DefaultDeleter__Ns3VendorSpecificValue_methods(root_module, root_module['ns3::DefaultDeleter< ns3::VendorSpecificValue >'])
register_Ns3DefaultDeleter__Ns3MmWaveChunkProcessor_methods(root_module, root_module['ns3::DefaultDeleter< ns3::MmWaveChunkProcessor >'])
register_Ns3DlCqiInfo_methods(root_module, root_module['ns3::DlCqiInfo'])
register_Ns3DlDciInfoElementTdma_methods(root_module, root_module['ns3::DlDciInfoElementTdma'])
register_Ns3DlDciListElement_s_methods(root_module, root_module['ns3::DlDciListElement_s'])
register_Ns3DlHarqInfo_methods(root_module, root_module['ns3::DlHarqInfo'])
register_Ns3DlInfoListElement_s_methods(root_module, root_module['ns3::DlInfoListElement_s'])
register_Ns3DlSchedulingCallbackInfo_methods(root_module, root_module['ns3::DlSchedulingCallbackInfo'])
register_Ns3DrxConfig_s_methods(root_module, root_module['ns3::DrxConfig_s'])
register_Ns3EnbPhyPacketCountParameter_methods(root_module, root_module['ns3::EnbPhyPacketCountParameter'])
register_Ns3EpcEnbS1SapProvider_methods(root_module, root_module['ns3::EpcEnbS1SapProvider'])
register_Ns3EpcEnbS1SapProviderBearerToBeSwitched_methods(root_module, root_module['ns3::EpcEnbS1SapProvider::BearerToBeSwitched'])
register_Ns3EpcEnbS1SapProviderPathSwitchRequestParameters_methods(root_module, root_module['ns3::EpcEnbS1SapProvider::PathSwitchRequestParameters'])
register_Ns3EpcEnbS1SapUser_methods(root_module, root_module['ns3::EpcEnbS1SapUser'])
register_Ns3EpcEnbS1SapUserDataRadioBearerSetupRequestParameters_methods(root_module, root_module['ns3::EpcEnbS1SapUser::DataRadioBearerSetupRequestParameters'])
register_Ns3EpcEnbS1SapUserPathSwitchRequestAcknowledgeParameters_methods(root_module, root_module['ns3::EpcEnbS1SapUser::PathSwitchRequestAcknowledgeParameters'])
register_Ns3EpcX2Sap_methods(root_module, root_module['ns3::EpcX2Sap'])
register_Ns3EpcX2SapCellInformationItem_methods(root_module, root_module['ns3::EpcX2Sap::CellInformationItem'])
register_Ns3EpcX2SapCellMeasurementResultItem_methods(root_module, root_module['ns3::EpcX2Sap::CellMeasurementResultItem'])
register_Ns3EpcX2SapCompositeAvailCapacity_methods(root_module, root_module['ns3::EpcX2Sap::CompositeAvailCapacity'])
register_Ns3EpcX2SapErabAdmittedItem_methods(root_module, root_module['ns3::EpcX2Sap::ErabAdmittedItem'])
register_Ns3EpcX2SapErabNotAdmittedItem_methods(root_module, root_module['ns3::EpcX2Sap::ErabNotAdmittedItem'])
register_Ns3EpcX2SapErabToBeSetupItem_methods(root_module, root_module['ns3::EpcX2Sap::ErabToBeSetupItem'])
register_Ns3EpcX2SapErabsSubjectToStatusTransferItem_methods(root_module, root_module['ns3::EpcX2Sap::ErabsSubjectToStatusTransferItem'])
register_Ns3EpcX2SapHandoverFailedParams_methods(root_module, root_module['ns3::EpcX2Sap::HandoverFailedParams'])
register_Ns3EpcX2SapHandoverPreparationFailureParams_methods(root_module, root_module['ns3::EpcX2Sap::HandoverPreparationFailureParams'])
register_Ns3EpcX2SapHandoverRequestAckParams_methods(root_module, root_module['ns3::EpcX2Sap::HandoverRequestAckParams'])
register_Ns3EpcX2SapHandoverRequestParams_methods(root_module, root_module['ns3::EpcX2Sap::HandoverRequestParams'])
register_Ns3EpcX2SapLoadInformationParams_methods(root_module, root_module['ns3::EpcX2Sap::LoadInformationParams'])
register_Ns3EpcX2SapRelativeNarrowbandTxBand_methods(root_module, root_module['ns3::EpcX2Sap::RelativeNarrowbandTxBand'])
register_Ns3EpcX2SapResourceStatusUpdateParams_methods(root_module, root_module['ns3::EpcX2Sap::ResourceStatusUpdateParams'])
register_Ns3EpcX2SapRlcSetupRequest_methods(root_module, root_module['ns3::EpcX2Sap::RlcSetupRequest'])
register_Ns3EpcX2SapSecondaryHandoverCompletedParams_methods(root_module, root_module['ns3::EpcX2Sap::SecondaryHandoverCompletedParams'])
register_Ns3EpcX2SapSecondaryHandoverParams_methods(root_module, root_module['ns3::EpcX2Sap::SecondaryHandoverParams'])
register_Ns3EpcX2SapSnStatusTransferParams_methods(root_module, root_module['ns3::EpcX2Sap::SnStatusTransferParams'])
register_Ns3EpcX2SapSwitchConnectionParams_methods(root_module, root_module['ns3::EpcX2Sap::SwitchConnectionParams'])
register_Ns3EpcX2SapUeContextReleaseParams_methods(root_module, root_module['ns3::EpcX2Sap::UeContextReleaseParams'])
register_Ns3EpcX2SapUeDataParams_methods(root_module, root_module['ns3::EpcX2Sap::UeDataParams'])
register_Ns3EpcX2SapUeImsiSinrParams_methods(root_module, root_module['ns3::EpcX2Sap::UeImsiSinrParams'])
register_Ns3EpcX2SapUlHighInterferenceInformationItem_methods(root_module, root_module['ns3::EpcX2Sap::UlHighInterferenceInformationItem'])
register_Ns3EpcX2SapProvider_methods(root_module, root_module['ns3::EpcX2SapProvider'])
register_Ns3EpcX2SapUser_methods(root_module, root_module['ns3::EpcX2SapUser'])
register_Ns3EpsBearer_methods(root_module, root_module['ns3::EpsBearer'])
register_Ns3EutranMeasurementMapping_methods(root_module, root_module['ns3::EutranMeasurementMapping'])
register_Ns3EventId_methods(root_module, root_module['ns3::EventId'])
register_Ns3ExpectedTbInfo_t_methods(root_module, root_module['ns3::ExpectedTbInfo_t'])
register_Ns3FfMacCschedSapProvider_methods(root_module, root_module['ns3::FfMacCschedSapProvider'])
register_Ns3FfMacCschedSapProviderCschedCellConfigReqParameters_methods(root_module, root_module['ns3::FfMacCschedSapProvider::CschedCellConfigReqParameters'])
register_Ns3FfMacCschedSapProviderCschedLcConfigReqParameters_methods(root_module, root_module['ns3::FfMacCschedSapProvider::CschedLcConfigReqParameters'])
register_Ns3FfMacCschedSapProviderCschedLcReleaseReqParameters_methods(root_module, root_module['ns3::FfMacCschedSapProvider::CschedLcReleaseReqParameters'])
register_Ns3FfMacCschedSapProviderCschedUeConfigReqParameters_methods(root_module, root_module['ns3::FfMacCschedSapProvider::CschedUeConfigReqParameters'])
register_Ns3FfMacCschedSapProviderCschedUeReleaseReqParameters_methods(root_module, root_module['ns3::FfMacCschedSapProvider::CschedUeReleaseReqParameters'])
register_Ns3FfMacCschedSapUser_methods(root_module, root_module['ns3::FfMacCschedSapUser'])
register_Ns3FfMacCschedSapUserCschedCellConfigCnfParameters_methods(root_module, root_module['ns3::FfMacCschedSapUser::CschedCellConfigCnfParameters'])
register_Ns3FfMacCschedSapUserCschedCellConfigUpdateIndParameters_methods(root_module, root_module['ns3::FfMacCschedSapUser::CschedCellConfigUpdateIndParameters'])
register_Ns3FfMacCschedSapUserCschedLcConfigCnfParameters_methods(root_module, root_module['ns3::FfMacCschedSapUser::CschedLcConfigCnfParameters'])
register_Ns3FfMacCschedSapUserCschedLcReleaseCnfParameters_methods(root_module, root_module['ns3::FfMacCschedSapUser::CschedLcReleaseCnfParameters'])
register_Ns3FfMacCschedSapUserCschedUeConfigCnfParameters_methods(root_module, root_module['ns3::FfMacCschedSapUser::CschedUeConfigCnfParameters'])
register_Ns3FfMacCschedSapUserCschedUeConfigUpdateIndParameters_methods(root_module, root_module['ns3::FfMacCschedSapUser::CschedUeConfigUpdateIndParameters'])
register_Ns3FfMacCschedSapUserCschedUeReleaseCnfParameters_methods(root_module, root_module['ns3::FfMacCschedSapUser::CschedUeReleaseCnfParameters'])
register_Ns3FfMacSchedSapProvider_methods(root_module, root_module['ns3::FfMacSchedSapProvider'])
register_Ns3FfMacSchedSapProviderSchedDlCqiInfoReqParameters_methods(root_module, root_module['ns3::FfMacSchedSapProvider::SchedDlCqiInfoReqParameters'])
register_Ns3FfMacSchedSapProviderSchedDlMacBufferReqParameters_methods(root_module, root_module['ns3::FfMacSchedSapProvider::SchedDlMacBufferReqParameters'])
register_Ns3FfMacSchedSapProviderSchedDlPagingBufferReqParameters_methods(root_module, root_module['ns3::FfMacSchedSapProvider::SchedDlPagingBufferReqParameters'])
register_Ns3FfMacSchedSapProviderSchedDlRachInfoReqParameters_methods(root_module, root_module['ns3::FfMacSchedSapProvider::SchedDlRachInfoReqParameters'])
register_Ns3FfMacSchedSapProviderSchedDlRlcBufferReqParameters_methods(root_module, root_module['ns3::FfMacSchedSapProvider::SchedDlRlcBufferReqParameters'])
register_Ns3FfMacSchedSapProviderSchedDlTriggerReqParameters_methods(root_module, root_module['ns3::FfMacSchedSapProvider::SchedDlTriggerReqParameters'])
register_Ns3FfMacSchedSapProviderSchedUlCqiInfoReqParameters_methods(root_module, root_module['ns3::FfMacSchedSapProvider::SchedUlCqiInfoReqParameters'])
register_Ns3FfMacSchedSapProviderSchedUlMacCtrlInfoReqParameters_methods(root_module, root_module['ns3::FfMacSchedSapProvider::SchedUlMacCtrlInfoReqParameters'])
register_Ns3FfMacSchedSapProviderSchedUlNoiseInterferenceReqParameters_methods(root_module, root_module['ns3::FfMacSchedSapProvider::SchedUlNoiseInterferenceReqParameters'])
register_Ns3FfMacSchedSapProviderSchedUlSrInfoReqParameters_methods(root_module, root_module['ns3::FfMacSchedSapProvider::SchedUlSrInfoReqParameters'])
register_Ns3FfMacSchedSapProviderSchedUlTriggerReqParameters_methods(root_module, root_module['ns3::FfMacSchedSapProvider::SchedUlTriggerReqParameters'])
register_Ns3FfMacSchedSapUser_methods(root_module, root_module['ns3::FfMacSchedSapUser'])
register_Ns3FfMacSchedSapUserSchedDlConfigIndParameters_methods(root_module, root_module['ns3::FfMacSchedSapUser::SchedDlConfigIndParameters'])
register_Ns3FfMacSchedSapUserSchedUlConfigIndParameters_methods(root_module, root_module['ns3::FfMacSchedSapUser::SchedUlConfigIndParameters'])
register_Ns3GbrQosInformation_methods(root_module, root_module['ns3::GbrQosInformation'])
register_Ns3HarqProcessInfoElement_t_methods(root_module, root_module['ns3::HarqProcessInfoElement_t'])
register_Ns3Hasher_methods(root_module, root_module['ns3::Hasher'])
register_Ns3HigherLayerSelected_s_methods(root_module, root_module['ns3::HigherLayerSelected_s'])
register_Ns3ImsiLcidPair_t_methods(root_module, root_module['ns3::ImsiLcidPair_t'])
register_Ns3Inet6SocketAddress_methods(root_module, root_module['ns3::Inet6SocketAddress'])
register_Ns3InetSocketAddress_methods(root_module, root_module['ns3::InetSocketAddress'])
register_Ns3Ipv4Address_methods(root_module, root_module['ns3::Ipv4Address'])
register_Ns3Ipv4AddressHelper_methods(root_module, root_module['ns3::Ipv4AddressHelper'])
register_Ns3Ipv4InterfaceAddress_methods(root_module, root_module['ns3::Ipv4InterfaceAddress'])
register_Ns3Ipv4InterfaceContainer_methods(root_module, root_module['ns3::Ipv4InterfaceContainer'])
register_Ns3Ipv4Mask_methods(root_module, root_module['ns3::Ipv4Mask'])
register_Ns3Ipv6Address_methods(root_module, root_module['ns3::Ipv6Address'])
register_Ns3Ipv6Prefix_methods(root_module, root_module['ns3::Ipv6Prefix'])
register_Ns3LogComponent_methods(root_module, root_module['ns3::LogComponent'])
register_Ns3LogicalChannelConfigListElement_s_methods(root_module, root_module['ns3::LogicalChannelConfigListElement_s'])
register_Ns3LteAnrSapProvider_methods(root_module, root_module['ns3::LteAnrSapProvider'])
register_Ns3LteAnrSapUser_methods(root_module, root_module['ns3::LteAnrSapUser'])
register_Ns3LteAsSapProvider_methods(root_module, root_module['ns3::LteAsSapProvider'])
register_Ns3LteAsSapUser_methods(root_module, root_module['ns3::LteAsSapUser'])
register_Ns3LteEnbCmacSapProvider_methods(root_module, root_module['ns3::LteEnbCmacSapProvider'])
register_Ns3LteEnbCmacSapProviderAllocateNcRaPreambleReturnValue_methods(root_module, root_module['ns3::LteEnbCmacSapProvider::AllocateNcRaPreambleReturnValue'])
register_Ns3LteEnbCmacSapProviderLcInfo_methods(root_module, root_module['ns3::LteEnbCmacSapProvider::LcInfo'])
register_Ns3LteEnbCmacSapProviderRachConfig_methods(root_module, root_module['ns3::LteEnbCmacSapProvider::RachConfig'])
register_Ns3LteEnbCmacSapProviderUeConfig_methods(root_module, root_module['ns3::LteEnbCmacSapProvider::UeConfig'])
register_Ns3LteEnbCmacSapUser_methods(root_module, root_module['ns3::LteEnbCmacSapUser'])
register_Ns3LteEnbCmacSapUserUeConfig_methods(root_module, root_module['ns3::LteEnbCmacSapUser::UeConfig'])
register_Ns3LteEnbCphySapProvider_methods(root_module, root_module['ns3::LteEnbCphySapProvider'])
register_Ns3LteEnbCphySapUser_methods(root_module, root_module['ns3::LteEnbCphySapUser'])
register_Ns3LteEnbCphySapUserUeAssociatedSinrInfo_methods(root_module, root_module['ns3::LteEnbCphySapUser::UeAssociatedSinrInfo'])
register_Ns3LteEnbPhySapProvider_methods(root_module, root_module['ns3::LteEnbPhySapProvider'])
register_Ns3LteEnbPhySapUser_methods(root_module, root_module['ns3::LteEnbPhySapUser'])
register_Ns3LteFfConverter_methods(root_module, root_module['ns3::LteFfConverter'])
register_Ns3LteFfrRrcSapProvider_methods(root_module, root_module['ns3::LteFfrRrcSapProvider'])
register_Ns3LteFfrRrcSapUser_methods(root_module, root_module['ns3::LteFfrRrcSapUser'])
register_Ns3LteFlowId_t_methods(root_module, root_module['ns3::LteFlowId_t'])
register_Ns3LteHandoverManagementSapProvider_methods(root_module, root_module['ns3::LteHandoverManagementSapProvider'])
register_Ns3LteHandoverManagementSapUser_methods(root_module, root_module['ns3::LteHandoverManagementSapUser'])
register_Ns3LteMacSapProvider_methods(root_module, root_module['ns3::LteMacSapProvider'])
register_Ns3LteMacSapProviderReportBufferStatusParameters_methods(root_module, root_module['ns3::LteMacSapProvider::ReportBufferStatusParameters'])
register_Ns3LteMacSapProviderTransmitPduParameters_methods(root_module, root_module['ns3::LteMacSapProvider::TransmitPduParameters'])
register_Ns3LteMacSapUser_methods(root_module, root_module['ns3::LteMacSapUser'])
register_Ns3LtePdcpSapProvider_methods(root_module, root_module['ns3::LtePdcpSapProvider'])
register_Ns3LtePdcpSapProviderTransmitPdcpSduParameters_methods(root_module, root_module['ns3::LtePdcpSapProvider::TransmitPdcpSduParameters'])
register_Ns3LtePdcpSapUser_methods(root_module, root_module['ns3::LtePdcpSapUser'])
register_Ns3LtePdcpSapUserReceivePdcpSduParameters_methods(root_module, root_module['ns3::LtePdcpSapUser::ReceivePdcpSduParameters'])
register_Ns3LteRlcSapProvider_methods(root_module, root_module['ns3::LteRlcSapProvider'])
register_Ns3LteRlcSapProviderTransmitPdcpPduParameters_methods(root_module, root_module['ns3::LteRlcSapProvider::TransmitPdcpPduParameters'])
register_Ns3LteRlcSapUser_methods(root_module, root_module['ns3::LteRlcSapUser'])
register_Ns3LteRlcSpecificLteMacSapUser_methods(root_module, root_module['ns3::LteRlcSpecificLteMacSapUser'])
register_Ns3LteRrcSap_methods(root_module, root_module['ns3::LteRrcSap'])
register_Ns3LteRrcSapAntennaInfoDedicated_methods(root_module, root_module['ns3::LteRrcSap::AntennaInfoDedicated'])
register_Ns3LteRrcSapAsConfig_methods(root_module, root_module['ns3::LteRrcSap::AsConfig'])
register_Ns3LteRrcSapBlackCellsToAddMod_methods(root_module, root_module['ns3::LteRrcSap::BlackCellsToAddMod'])
register_Ns3LteRrcSapCarrierBandwidthEutra_methods(root_module, root_module['ns3::LteRrcSap::CarrierBandwidthEutra'])
register_Ns3LteRrcSapCarrierFreqEutra_methods(root_module, root_module['ns3::LteRrcSap::CarrierFreqEutra'])
register_Ns3LteRrcSapCellAccessRelatedInfo_methods(root_module, root_module['ns3::LteRrcSap::CellAccessRelatedInfo'])
register_Ns3LteRrcSapCellSelectionInfo_methods(root_module, root_module['ns3::LteRrcSap::CellSelectionInfo'])
register_Ns3LteRrcSapCellsToAddMod_methods(root_module, root_module['ns3::LteRrcSap::CellsToAddMod'])
register_Ns3LteRrcSapCgiInfo_methods(root_module, root_module['ns3::LteRrcSap::CgiInfo'])
register_Ns3LteRrcSapDrbToAddMod_methods(root_module, root_module['ns3::LteRrcSap::DrbToAddMod'])
register_Ns3LteRrcSapFreqInfo_methods(root_module, root_module['ns3::LteRrcSap::FreqInfo'])
register_Ns3LteRrcSapHandoverPreparationInfo_methods(root_module, root_module['ns3::LteRrcSap::HandoverPreparationInfo'])
register_Ns3LteRrcSapLogicalChannelConfig_methods(root_module, root_module['ns3::LteRrcSap::LogicalChannelConfig'])
register_Ns3LteRrcSapMasterInformationBlock_methods(root_module, root_module['ns3::LteRrcSap::MasterInformationBlock'])
register_Ns3LteRrcSapMeasConfig_methods(root_module, root_module['ns3::LteRrcSap::MeasConfig'])
register_Ns3LteRrcSapMeasGapConfig_methods(root_module, root_module['ns3::LteRrcSap::MeasGapConfig'])
register_Ns3LteRrcSapMeasIdToAddMod_methods(root_module, root_module['ns3::LteRrcSap::MeasIdToAddMod'])
register_Ns3LteRrcSapMeasObjectEutra_methods(root_module, root_module['ns3::LteRrcSap::MeasObjectEutra'])
register_Ns3LteRrcSapMeasObjectToAddMod_methods(root_module, root_module['ns3::LteRrcSap::MeasObjectToAddMod'])
register_Ns3LteRrcSapMeasResultEutra_methods(root_module, root_module['ns3::LteRrcSap::MeasResultEutra'])
register_Ns3LteRrcSapMeasResults_methods(root_module, root_module['ns3::LteRrcSap::MeasResults'])
register_Ns3LteRrcSapMeasurementReport_methods(root_module, root_module['ns3::LteRrcSap::MeasurementReport'])
register_Ns3LteRrcSapMobilityControlInfo_methods(root_module, root_module['ns3::LteRrcSap::MobilityControlInfo'])
register_Ns3LteRrcSapMobilityStateParameters_methods(root_module, root_module['ns3::LteRrcSap::MobilityStateParameters'])
register_Ns3LteRrcSapPdschConfigCommon_methods(root_module, root_module['ns3::LteRrcSap::PdschConfigCommon'])
register_Ns3LteRrcSapPdschConfigDedicated_methods(root_module, root_module['ns3::LteRrcSap::PdschConfigDedicated'])
register_Ns3LteRrcSapPhysCellIdRange_methods(root_module, root_module['ns3::LteRrcSap::PhysCellIdRange'])
register_Ns3LteRrcSapPhysicalConfigDedicated_methods(root_module, root_module['ns3::LteRrcSap::PhysicalConfigDedicated'])
register_Ns3LteRrcSapPlmnIdentityInfo_methods(root_module, root_module['ns3::LteRrcSap::PlmnIdentityInfo'])
register_Ns3LteRrcSapPreambleInfo_methods(root_module, root_module['ns3::LteRrcSap::PreambleInfo'])
register_Ns3LteRrcSapQuantityConfig_methods(root_module, root_module['ns3::LteRrcSap::QuantityConfig'])
register_Ns3LteRrcSapRaSupervisionInfo_methods(root_module, root_module['ns3::LteRrcSap::RaSupervisionInfo'])
register_Ns3LteRrcSapRachConfigCommon_methods(root_module, root_module['ns3::LteRrcSap::RachConfigCommon'])
register_Ns3LteRrcSapRachConfigDedicated_methods(root_module, root_module['ns3::LteRrcSap::RachConfigDedicated'])
register_Ns3LteRrcSapRadioResourceConfigCommon_methods(root_module, root_module['ns3::LteRrcSap::RadioResourceConfigCommon'])
register_Ns3LteRrcSapRadioResourceConfigCommonSib_methods(root_module, root_module['ns3::LteRrcSap::RadioResourceConfigCommonSib'])
register_Ns3LteRrcSapRadioResourceConfigDedicated_methods(root_module, root_module['ns3::LteRrcSap::RadioResourceConfigDedicated'])
register_Ns3LteRrcSapReestabUeIdentity_methods(root_module, root_module['ns3::LteRrcSap::ReestabUeIdentity'])
register_Ns3LteRrcSapReportConfigEutra_methods(root_module, root_module['ns3::LteRrcSap::ReportConfigEutra'])
register_Ns3LteRrcSapReportConfigToAddMod_methods(root_module, root_module['ns3::LteRrcSap::ReportConfigToAddMod'])
register_Ns3LteRrcSapRlcConfig_methods(root_module, root_module['ns3::LteRrcSap::RlcConfig'])
register_Ns3LteRrcSapRrcConnectionReconfiguration_methods(root_module, root_module['ns3::LteRrcSap::RrcConnectionReconfiguration'])
register_Ns3LteRrcSapRrcConnectionReconfigurationCompleted_methods(root_module, root_module['ns3::LteRrcSap::RrcConnectionReconfigurationCompleted'])
register_Ns3LteRrcSapRrcConnectionReestablishment_methods(root_module, root_module['ns3::LteRrcSap::RrcConnectionReestablishment'])
register_Ns3LteRrcSapRrcConnectionReestablishmentComplete_methods(root_module, root_module['ns3::LteRrcSap::RrcConnectionReestablishmentComplete'])
register_Ns3LteRrcSapRrcConnectionReestablishmentReject_methods(root_module, root_module['ns3::LteRrcSap::RrcConnectionReestablishmentReject'])
register_Ns3LteRrcSapRrcConnectionReestablishmentRequest_methods(root_module, root_module['ns3::LteRrcSap::RrcConnectionReestablishmentRequest'])
register_Ns3LteRrcSapRrcConnectionReject_methods(root_module, root_module['ns3::LteRrcSap::RrcConnectionReject'])
register_Ns3LteRrcSapRrcConnectionRelease_methods(root_module, root_module['ns3::LteRrcSap::RrcConnectionRelease'])
register_Ns3LteRrcSapRrcConnectionRequest_methods(root_module, root_module['ns3::LteRrcSap::RrcConnectionRequest'])
register_Ns3LteRrcSapRrcConnectionSetup_methods(root_module, root_module['ns3::LteRrcSap::RrcConnectionSetup'])
register_Ns3LteRrcSapRrcConnectionSetupCompleted_methods(root_module, root_module['ns3::LteRrcSap::RrcConnectionSetupCompleted'])
register_Ns3LteRrcSapRrcConnectionSwitch_methods(root_module, root_module['ns3::LteRrcSap::RrcConnectionSwitch'])
register_Ns3LteRrcSapSoundingRsUlConfigCommon_methods(root_module, root_module['ns3::LteRrcSap::SoundingRsUlConfigCommon'])
register_Ns3LteRrcSapSoundingRsUlConfigDedicated_methods(root_module, root_module['ns3::LteRrcSap::SoundingRsUlConfigDedicated'])
register_Ns3LteRrcSapSpeedStatePars_methods(root_module, root_module['ns3::LteRrcSap::SpeedStatePars'])
register_Ns3LteRrcSapSpeedStateScaleFactors_methods(root_module, root_module['ns3::LteRrcSap::SpeedStateScaleFactors'])
register_Ns3LteRrcSapSrbToAddMod_methods(root_module, root_module['ns3::LteRrcSap::SrbToAddMod'])
register_Ns3LteRrcSapSystemInformation_methods(root_module, root_module['ns3::LteRrcSap::SystemInformation'])
register_Ns3LteRrcSapSystemInformationBlockType1_methods(root_module, root_module['ns3::LteRrcSap::SystemInformationBlockType1'])
register_Ns3LteRrcSapSystemInformationBlockType2_methods(root_module, root_module['ns3::LteRrcSap::SystemInformationBlockType2'])
register_Ns3LteRrcSapThresholdEutra_methods(root_module, root_module['ns3::LteRrcSap::ThresholdEutra'])
register_Ns3LteSpectrumValueHelper_methods(root_module, root_module['ns3::LteSpectrumValueHelper'])
register_Ns3LteUeCmacSapProvider_methods(root_module, root_module['ns3::LteUeCmacSapProvider'])
register_Ns3LteUeCmacSapProviderLogicalChannelConfig_methods(root_module, root_module['ns3::LteUeCmacSapProvider::LogicalChannelConfig'])
register_Ns3LteUeCmacSapProviderRachConfig_methods(root_module, root_module['ns3::LteUeCmacSapProvider::RachConfig'])
register_Ns3LteUeCmacSapUser_methods(root_module, root_module['ns3::LteUeCmacSapUser'])
register_Ns3LteUeConfig_t_methods(root_module, root_module['ns3::LteUeConfig_t'])
register_Ns3LteUeCphySapProvider_methods(root_module, root_module['ns3::LteUeCphySapProvider'])
register_Ns3LteUeCphySapUser_methods(root_module, root_module['ns3::LteUeCphySapUser'])
register_Ns3LteUeCphySapUserUeMeasurementsElement_methods(root_module, root_module['ns3::LteUeCphySapUser::UeMeasurementsElement'])
register_Ns3LteUeCphySapUserUeMeasurementsParameters_methods(root_module, root_module['ns3::LteUeCphySapUser::UeMeasurementsParameters'])
register_Ns3LteUePhySapProvider_methods(root_module, root_module['ns3::LteUePhySapProvider'])
register_Ns3LteUePhySapUser_methods(root_module, root_module['ns3::LteUePhySapUser'])
register_Ns3LteUeRrcSapProvider_methods(root_module, root_module['ns3::LteUeRrcSapProvider'])
register_Ns3LteUeRrcSapProviderCompleteSetupParameters_methods(root_module, root_module['ns3::LteUeRrcSapProvider::CompleteSetupParameters'])
register_Ns3LteUeRrcSapUser_methods(root_module, root_module['ns3::LteUeRrcSapUser'])
register_Ns3LteUeRrcSapUserSetupParameters_methods(root_module, root_module['ns3::LteUeRrcSapUser::SetupParameters'])
register_Ns3Mac48Address_methods(root_module, root_module['ns3::Mac48Address'])
register_Ns3MacCeElement_methods(root_module, root_module['ns3::MacCeElement'])
register_Ns3MacCeListElement_s_methods(root_module, root_module['ns3::MacCeListElement_s'])
register_Ns3MacCeValue_methods(root_module, root_module['ns3::MacCeValue'])
register_Ns3MacCeValue_u_methods(root_module, root_module['ns3::MacCeValue_u'])
register_Ns3MacPduInfo_methods(root_module, root_module['ns3::MacPduInfo'])
register_Ns3MacSubheader_methods(root_module, root_module['ns3::MacSubheader'])
register_Ns3MmWaveDlHarqProcessInfo_methods(root_module, root_module['ns3::MmWaveDlHarqProcessInfo'])
register_Ns3MmWaveEnbPhySapUser_methods(root_module, root_module['ns3::MmWaveEnbPhySapUser'])
register_Ns3MmWaveHarqProcessInfoElement_t_methods(root_module, root_module['ns3::MmWaveHarqProcessInfoElement_t'])
register_Ns3MmWaveMacCschedSapProvider_methods(root_module, root_module['ns3::MmWaveMacCschedSapProvider'])
register_Ns3MmWaveMacCschedSapProviderCschedCellConfigReqParameters_methods(root_module, root_module['ns3::MmWaveMacCschedSapProvider::CschedCellConfigReqParameters'])
register_Ns3MmWaveMacCschedSapProviderCschedLcConfigReqParameters_methods(root_module, root_module['ns3::MmWaveMacCschedSapProvider::CschedLcConfigReqParameters'])
register_Ns3MmWaveMacCschedSapProviderCschedLcReleaseReqParameters_methods(root_module, root_module['ns3::MmWaveMacCschedSapProvider::CschedLcReleaseReqParameters'])
register_Ns3MmWaveMacCschedSapProviderCschedUeConfigReqParameters_methods(root_module, root_module['ns3::MmWaveMacCschedSapProvider::CschedUeConfigReqParameters'])
register_Ns3MmWaveMacCschedSapProviderCschedUeReleaseReqParameters_methods(root_module, root_module['ns3::MmWaveMacCschedSapProvider::CschedUeReleaseReqParameters'])
register_Ns3MmWaveMacCschedSapUser_methods(root_module, root_module['ns3::MmWaveMacCschedSapUser'])
register_Ns3MmWaveMacCschedSapUserCschedCellConfigCnfParameters_methods(root_module, root_module['ns3::MmWaveMacCschedSapUser::CschedCellConfigCnfParameters'])
register_Ns3MmWaveMacCschedSapUserCschedCellConfigUpdateIndParameters_methods(root_module, root_module['ns3::MmWaveMacCschedSapUser::CschedCellConfigUpdateIndParameters'])
register_Ns3MmWaveMacCschedSapUserCschedLcConfigCnfParameters_methods(root_module, root_module['ns3::MmWaveMacCschedSapUser::CschedLcConfigCnfParameters'])
register_Ns3MmWaveMacCschedSapUserCschedLcReleaseCnfParameters_methods(root_module, root_module['ns3::MmWaveMacCschedSapUser::CschedLcReleaseCnfParameters'])
register_Ns3MmWaveMacCschedSapUserCschedUeConfigCnfParameters_methods(root_module, root_module['ns3::MmWaveMacCschedSapUser::CschedUeConfigCnfParameters'])
register_Ns3MmWaveMacCschedSapUserCschedUeConfigUpdateIndParameters_methods(root_module, root_module['ns3::MmWaveMacCschedSapUser::CschedUeConfigUpdateIndParameters'])
register_Ns3MmWaveMacCschedSapUserCschedUeReleaseCnfParameters_methods(root_module, root_module['ns3::MmWaveMacCschedSapUser::CschedUeReleaseCnfParameters'])
register_Ns3MmWaveMacSchedSapProvider_methods(root_module, root_module['ns3::MmWaveMacSchedSapProvider'])
register_Ns3MmWaveMacSchedSapProviderSchedDlCqiInfoReqParameters_methods(root_module, root_module['ns3::MmWaveMacSchedSapProvider::SchedDlCqiInfoReqParameters'])
register_Ns3MmWaveMacSchedSapProviderSchedDlRlcBufferReqParameters_methods(root_module, root_module['ns3::MmWaveMacSchedSapProvider::SchedDlRlcBufferReqParameters'])
register_Ns3MmWaveMacSchedSapProviderSchedTriggerReqParameters_methods(root_module, root_module['ns3::MmWaveMacSchedSapProvider::SchedTriggerReqParameters'])
register_Ns3MmWaveMacSchedSapProviderSchedUlCqiInfoReqParameters_methods(root_module, root_module['ns3::MmWaveMacSchedSapProvider::SchedUlCqiInfoReqParameters'])
register_Ns3MmWaveMacSchedSapProviderSchedUlMacCtrlInfoReqParameters_methods(root_module, root_module['ns3::MmWaveMacSchedSapProvider::SchedUlMacCtrlInfoReqParameters'])
register_Ns3MmWaveMacSchedSapUser_methods(root_module, root_module['ns3::MmWaveMacSchedSapUser'])
register_Ns3MmWaveMacSchedSapUserSchedConfigIndParameters_methods(root_module, root_module['ns3::MmWaveMacSchedSapUser::SchedConfigIndParameters'])
register_Ns3MmWaveMiErrorModel_methods(root_module, root_module['ns3::MmWaveMiErrorModel'])
register_Ns3MmWavePhySapProvider_methods(root_module, root_module['ns3::MmWavePhySapProvider'])
register_Ns3MmWaveRealProtocolRlcSapUser_methods(root_module, root_module['ns3::MmWaveRealProtocolRlcSapUser'])
register_Ns3MmWaveSpectrumValueHelper_methods(root_module, root_module['ns3::MmWaveSpectrumValueHelper'])
register_Ns3MmWaveTbStats_t_methods(root_module, root_module['ns3::MmWaveTbStats_t'])
register_Ns3MmWaveUePhySapUser_methods(root_module, root_module['ns3::MmWaveUePhySapUser'])
register_Ns3Names_methods(root_module, root_module['ns3::Names'])
register_Ns3NetDeviceContainer_methods(root_module, root_module['ns3::NetDeviceContainer'])
register_Ns3NodeContainer_methods(root_module, root_module['ns3::NodeContainer'])
register_Ns3ObjectBase_methods(root_module, root_module['ns3::ObjectBase'])
register_Ns3ObjectDeleter_methods(root_module, root_module['ns3::ObjectDeleter'])
register_Ns3ObjectFactory_methods(root_module, root_module['ns3::ObjectFactory'])
register_Ns3PacketMetadata_methods(root_module, root_module['ns3::PacketMetadata'])
register_Ns3PacketMetadataItem_methods(root_module, root_module['ns3::PacketMetadata::Item'])
register_Ns3PacketMetadataItemIterator_methods(root_module, root_module['ns3::PacketMetadata::ItemIterator'])
register_Ns3PacketTagIterator_methods(root_module, root_module['ns3::PacketTagIterator'])
register_Ns3PacketTagIteratorItem_methods(root_module, root_module['ns3::PacketTagIterator::Item'])
register_Ns3PacketTagList_methods(root_module, root_module['ns3::PacketTagList'])
register_Ns3PacketTagListTagData_methods(root_module, root_module['ns3::PacketTagList::TagData'])
register_Ns3PagingInfoListElement_s_methods(root_module, root_module['ns3::PagingInfoListElement_s'])
register_Ns3ParameterLogger_methods(root_module, root_module['ns3::ParameterLogger'])
register_Ns3PhichListElement_s_methods(root_module, root_module['ns3::PhichListElement_s'])
register_Ns3PhyReceptionStatParameters_methods(root_module, root_module['ns3::PhyReceptionStatParameters'])
register_Ns3PhyTransmissionStatParameters_methods(root_module, root_module['ns3::PhyTransmissionStatParameters'])
register_Ns3RachListElement_s_methods(root_module, root_module['ns3::RachListElement_s'])
register_Ns3RlcListElement_methods(root_module, root_module['ns3::RlcListElement'])
register_Ns3RlcPduInfo_methods(root_module, root_module['ns3::RlcPduInfo'])
register_Ns3RlcPduListElement_s_methods(root_module, root_module['ns3::RlcPduListElement_s'])
register_Ns3RxPacketTraceParams_methods(root_module, root_module['ns3::RxPacketTraceParams'])
register_Ns3SbMeasResult_s_methods(root_module, root_module['ns3::SbMeasResult_s'])
register_Ns3SchedInfo_methods(root_module, root_module['ns3::SchedInfo'])
register_Ns3SequenceNumber10_methods(root_module, root_module['ns3::SequenceNumber10'])
register_Ns3SfAllocInfo_methods(root_module, root_module['ns3::SfAllocInfo'])
register_Ns3SfnSf_methods(root_module, root_module['ns3::SfnSf'])
register_Ns3SiConfiguration_s_methods(root_module, root_module['ns3::SiConfiguration_s'])
register_Ns3SiMessageListElement_s_methods(root_module, root_module['ns3::SiMessageListElement_s'])
register_Ns3SimpleRefCount__Ns3Object_Ns3ObjectBase_Ns3ObjectDeleter_methods(root_module, root_module['ns3::SimpleRefCount< ns3::Object, ns3::ObjectBase, ns3::ObjectDeleter >'])
register_Ns3Simulator_methods(root_module, root_module['ns3::Simulator'])
register_Ns3SlotAllocInfo_methods(root_module, root_module['ns3::SlotAllocInfo'])
register_Ns3SpsConfig_s_methods(root_module, root_module['ns3::SpsConfig_s'])
register_Ns3SrConfig_s_methods(root_module, root_module['ns3::SrConfig_s'])
register_Ns3SrListElement_s_methods(root_module, root_module['ns3::SrListElement_s'])
register_Ns3StatisticalSummary_methods(root_module, root_module['ns3::StatisticalSummary'])
register_Ns3Tag_methods(root_module, root_module['ns3::Tag'])
register_Ns3TagBuffer_methods(root_module, root_module['ns3::TagBuffer'])
register_Ns3TbAllocInfo_methods(root_module, root_module['ns3::TbAllocInfo'])
register_Ns3TbId_t_methods(root_module, root_module['ns3::TbId_t'])
register_Ns3TbInfoElement_methods(root_module, root_module['ns3::TbInfoElement'])
register_Ns3TimeWithUnit_methods(root_module, root_module['ns3::TimeWithUnit'])
register_Ns3TracedValue__Bool_methods(root_module, root_module['ns3::TracedValue< bool >'])
register_Ns3TracedValue__Unsigned_int_methods(root_module, root_module['ns3::TracedValue< unsigned int >'])
register_Ns3TransmissionModesLayers_methods(root_module, root_module['ns3::TransmissionModesLayers'])
register_Ns3TypeId_methods(root_module, root_module['ns3::TypeId'])
register_Ns3TypeIdAttributeInformation_methods(root_module, root_module['ns3::TypeId::AttributeInformation'])
register_Ns3TypeIdTraceSourceInformation_methods(root_module, root_module['ns3::TypeId::TraceSourceInformation'])
register_Ns3UeCapabilities_s_methods(root_module, root_module['ns3::UeCapabilities_s'])
register_Ns3UePhyPacketCountParameter_methods(root_module, root_module['ns3::UePhyPacketCountParameter'])
register_Ns3UeSelected_s_methods(root_module, root_module['ns3::UeSelected_s'])
register_Ns3UlCqiInfo_methods(root_module, root_module['ns3::UlCqiInfo'])
register_Ns3UlCqi_s_methods(root_module, root_module['ns3::UlCqi_s'])
register_Ns3UlDciListElement_s_methods(root_module, root_module['ns3::UlDciListElement_s'])
register_Ns3UlGrant_s_methods(root_module, root_module['ns3::UlGrant_s'])
register_Ns3UlHarqInfo_methods(root_module, root_module['ns3::UlHarqInfo'])
register_Ns3UlInfoListElement_s_methods(root_module, root_module['ns3::UlInfoListElement_s'])
register_Ns3Vector2D_methods(root_module, root_module['ns3::Vector2D'])
register_Ns3Vector3D_methods(root_module, root_module['ns3::Vector3D'])
register_Ns3VendorSpecificListElement_s_methods(root_module, root_module['ns3::VendorSpecificListElement_s'])
register_Ns3Empty_methods(root_module, root_module['ns3::empty'])
register_Ns3Int64x64_t_methods(root_module, root_module['ns3::int64x64_t'])
register_Ns3TbInfo_t_methods(root_module, root_module['ns3::tbInfo_t'])
register_Ns3Chunk_methods(root_module, root_module['ns3::Chunk'])
register_Ns3EpcX2PdcpProvider_methods(root_module, root_module['ns3::EpcX2PdcpProvider'])
register_Ns3EpcX2PdcpUser_methods(root_module, root_module['ns3::EpcX2PdcpUser'])
register_Ns3EpcX2RlcProvider_methods(root_module, root_module['ns3::EpcX2RlcProvider'])
register_Ns3EpcX2RlcUser_methods(root_module, root_module['ns3::EpcX2RlcUser'])
register_Ns3Header_methods(root_module, root_module['ns3::Header'])
register_Ns3LteEnbRrcSapProvider_methods(root_module, root_module['ns3::LteEnbRrcSapProvider'])
register_Ns3LteEnbRrcSapProviderCompleteSetupUeParameters_methods(root_module, root_module['ns3::LteEnbRrcSapProvider::CompleteSetupUeParameters'])
register_Ns3LteEnbRrcSapUser_methods(root_module, root_module['ns3::LteEnbRrcSapUser'])
register_Ns3LteEnbRrcSapUserSetupUeParameters_methods(root_module, root_module['ns3::LteEnbRrcSapUser::SetupUeParameters'])
register_Ns3LtePdcpHeader_methods(root_module, root_module['ns3::LtePdcpHeader'])
register_Ns3LteRadioBearerTag_methods(root_module, root_module['ns3::LteRadioBearerTag'])
register_Ns3MmWaveMacPduHeader_methods(root_module, root_module['ns3::MmWaveMacPduHeader'])
register_Ns3MmWaveMacPduTag_methods(root_module, root_module['ns3::MmWaveMacPduTag'])
register_Ns3MmWaveRadioBearerTag_methods(root_module, root_module['ns3::MmWaveRadioBearerTag'])
register_Ns3Object_methods(root_module, root_module['ns3::Object'])
register_Ns3ObjectAggregateIterator_methods(root_module, root_module['ns3::Object::AggregateIterator'])
register_Ns3PacketBurst_methods(root_module, root_module['ns3::PacketBurst'])
register_Ns3PacketFilter_methods(root_module, root_module['ns3::PacketFilter'])
register_Ns3ParamsTable_methods(root_module, root_module['ns3::ParamsTable'])
register_Ns3PropagationLossModel_methods(root_module, root_module['ns3::PropagationLossModel'])
register_Ns3QueueDisc_methods(root_module, root_module['ns3::QueueDisc'])
register_Ns3QueueDiscStats_methods(root_module, root_module['ns3::QueueDisc::Stats'])
register_Ns3QueueDiscClass_methods(root_module, root_module['ns3::QueueDiscClass'])
register_Ns3RandomPropagationLossModel_methods(root_module, root_module['ns3::RandomPropagationLossModel'])
register_Ns3RandomVariableStream_methods(root_module, root_module['ns3::RandomVariableStream'])
register_Ns3RangePropagationLossModel_methods(root_module, root_module['ns3::RangePropagationLossModel'])
register_Ns3RlcBearerInfo_methods(root_module, root_module['ns3::RlcBearerInfo'])
register_Ns3SequentialRandomVariable_methods(root_module, root_module['ns3::SequentialRandomVariable'])
register_Ns3SimpleRefCount__Ns3AttributeAccessor_Ns3Empty_Ns3DefaultDeleter__lt__ns3AttributeAccessor__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::AttributeAccessor, ns3::empty, ns3::DefaultDeleter<ns3::AttributeAccessor> >'])
register_Ns3SimpleRefCount__Ns3AttributeChecker_Ns3Empty_Ns3DefaultDeleter__lt__ns3AttributeChecker__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::AttributeChecker, ns3::empty, ns3::DefaultDeleter<ns3::AttributeChecker> >'])
register_Ns3SimpleRefCount__Ns3AttributeValue_Ns3Empty_Ns3DefaultDeleter__lt__ns3AttributeValue__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::AttributeValue, ns3::empty, ns3::DefaultDeleter<ns3::AttributeValue> >'])
register_Ns3SimpleRefCount__Ns3BeamformingParams_Ns3Empty_Ns3DefaultDeleter__lt__ns3BeamformingParams__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::BeamformingParams, ns3::empty, ns3::DefaultDeleter<ns3::BeamformingParams> >'])
register_Ns3SimpleRefCount__Ns3CallbackImplBase_Ns3Empty_Ns3DefaultDeleter__lt__ns3CallbackImplBase__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::CallbackImplBase, ns3::empty, ns3::DefaultDeleter<ns3::CallbackImplBase> >'])
register_Ns3SimpleRefCount__Ns3ChannelParams_Ns3Empty_Ns3DefaultDeleter__lt__ns3ChannelParams__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::ChannelParams, ns3::empty, ns3::DefaultDeleter<ns3::ChannelParams> >'])
register_Ns3SimpleRefCount__Ns3EpcTft_Ns3Empty_Ns3DefaultDeleter__lt__ns3EpcTft__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::EpcTft, ns3::empty, ns3::DefaultDeleter<ns3::EpcTft> >'])
register_Ns3SimpleRefCount__Ns3EpcTftClassifier_Ns3Empty_Ns3DefaultDeleter__lt__ns3EpcTftClassifier__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::EpcTftClassifier, ns3::empty, ns3::DefaultDeleter<ns3::EpcTftClassifier> >'])
register_Ns3SimpleRefCount__Ns3EventImpl_Ns3Empty_Ns3DefaultDeleter__lt__ns3EventImpl__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::EventImpl, ns3::empty, ns3::DefaultDeleter<ns3::EventImpl> >'])
register_Ns3SimpleRefCount__Ns3HashImplementation_Ns3Empty_Ns3DefaultDeleter__lt__ns3HashImplementation__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::Hash::Implementation, ns3::empty, ns3::DefaultDeleter<ns3::Hash::Implementation> >'])
register_Ns3SimpleRefCount__Ns3Ipv4MulticastRoute_Ns3Empty_Ns3DefaultDeleter__lt__ns3Ipv4MulticastRoute__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::Ipv4MulticastRoute, ns3::empty, ns3::DefaultDeleter<ns3::Ipv4MulticastRoute> >'])
register_Ns3SimpleRefCount__Ns3Ipv4Route_Ns3Empty_Ns3DefaultDeleter__lt__ns3Ipv4Route__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::Ipv4Route, ns3::empty, ns3::DefaultDeleter<ns3::Ipv4Route> >'])
register_Ns3SimpleRefCount__Ns3LteControlMessage_Ns3Empty_Ns3DefaultDeleter__lt__ns3LteControlMessage__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::LteControlMessage, ns3::empty, ns3::DefaultDeleter<ns3::LteControlMessage> >'])
register_Ns3SimpleRefCount__Ns3LteHarqPhy_Ns3Empty_Ns3DefaultDeleter__lt__ns3LteHarqPhy__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::LteHarqPhy, ns3::empty, ns3::DefaultDeleter<ns3::LteHarqPhy> >'])
register_Ns3SimpleRefCount__Ns3MmWaveControlMessage_Ns3Empty_Ns3DefaultDeleter__lt__ns3MmWaveControlMessage__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::MmWaveControlMessage, ns3::empty, ns3::DefaultDeleter<ns3::MmWaveControlMessage> >'])
register_Ns3SimpleRefCount__Ns3MmWaveHarqPhy_Ns3Empty_Ns3DefaultDeleter__lt__ns3MmWaveHarqPhy__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::MmWaveHarqPhy, ns3::empty, ns3::DefaultDeleter<ns3::MmWaveHarqPhy> >'])
register_Ns3SimpleRefCount__Ns3NixVector_Ns3Empty_Ns3DefaultDeleter__lt__ns3NixVector__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::NixVector, ns3::empty, ns3::DefaultDeleter<ns3::NixVector> >'])
register_Ns3SimpleRefCount__Ns3Packet_Ns3Empty_Ns3DefaultDeleter__lt__ns3Packet__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::Packet, ns3::empty, ns3::DefaultDeleter<ns3::Packet> >'])
register_Ns3SimpleRefCount__Ns3Params3gpp_Ns3Empty_Ns3DefaultDeleter__lt__ns3Params3gpp__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::Params3gpp, ns3::empty, ns3::DefaultDeleter<ns3::Params3gpp> >'])
register_Ns3SimpleRefCount__Ns3QueueItem_Ns3Empty_Ns3DefaultDeleter__lt__ns3QueueItem__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::QueueItem, ns3::empty, ns3::DefaultDeleter<ns3::QueueItem> >'])
register_Ns3SimpleRefCount__Ns3SpectrumModel_Ns3Empty_Ns3DefaultDeleter__lt__ns3SpectrumModel__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::SpectrumModel, ns3::empty, ns3::DefaultDeleter<ns3::SpectrumModel> >'])
register_Ns3SimpleRefCount__Ns3SpectrumSignalParameters_Ns3Empty_Ns3DefaultDeleter__lt__ns3SpectrumSignalParameters__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::SpectrumSignalParameters, ns3::empty, ns3::DefaultDeleter<ns3::SpectrumSignalParameters> >'])
register_Ns3SimpleRefCount__Ns3SpectrumValue_Ns3Empty_Ns3DefaultDeleter__lt__ns3SpectrumValue__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::SpectrumValue, ns3::empty, ns3::DefaultDeleter<ns3::SpectrumValue> >'])
register_Ns3SimpleRefCount__Ns3TraceParams_Ns3Empty_Ns3DefaultDeleter__lt__ns3TraceParams__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::TraceParams, ns3::empty, ns3::DefaultDeleter<ns3::TraceParams> >'])
register_Ns3SimpleRefCount__Ns3TraceSourceAccessor_Ns3Empty_Ns3DefaultDeleter__lt__ns3TraceSourceAccessor__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::TraceSourceAccessor, ns3::empty, ns3::DefaultDeleter<ns3::TraceSourceAccessor> >'])
register_Ns3SimpleRefCount__Ns3VendorSpecificValue_Ns3Empty_Ns3DefaultDeleter__lt__ns3VendorSpecificValue__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::VendorSpecificValue, ns3::empty, ns3::DefaultDeleter<ns3::VendorSpecificValue> >'])
register_Ns3SimpleRefCount__Ns3ChannelMatrix_Ns3Empty_Ns3DefaultDeleter__lt__ns3ChannelMatrix__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::channelMatrix, ns3::empty, ns3::DefaultDeleter<ns3::channelMatrix> >'])
register_Ns3SimpleRefCount__Ns3MmWaveBeamFormingParams_Ns3Empty_Ns3DefaultDeleter__lt__ns3MmWaveBeamFormingParams__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::mmWaveBeamFormingParams, ns3::empty, ns3::DefaultDeleter<ns3::mmWaveBeamFormingParams> >'])
register_Ns3SimpleRefCount__Ns3MmWaveBeamFormingTraces_Ns3Empty_Ns3DefaultDeleter__lt__ns3MmWaveBeamFormingTraces__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::mmWaveBeamFormingTraces, ns3::empty, ns3::DefaultDeleter<ns3::mmWaveBeamFormingTraces> >'])
register_Ns3SimpleRefCount__Ns3MmWaveChunkProcessor_Ns3Empty_Ns3DefaultDeleter__lt__ns3MmWaveChunkProcessor__gt___methods(root_module, root_module['ns3::SimpleRefCount< ns3::MmWaveChunkProcessor, ns3::empty, ns3::DefaultDeleter<ns3::MmWaveChunkProcessor> >'])
register_Ns3Socket_methods(root_module, root_module['ns3::Socket'])
register_Ns3SocketIpTosTag_methods(root_module, root_module['ns3::SocketIpTosTag'])
register_Ns3SocketIpTtlTag_methods(root_module, root_module['ns3::SocketIpTtlTag'])
register_Ns3SocketIpv6HopLimitTag_methods(root_module, root_module['ns3::SocketIpv6HopLimitTag'])
register_Ns3SocketIpv6TclassTag_methods(root_module, root_module['ns3::SocketIpv6TclassTag'])
register_Ns3SocketPriorityTag_methods(root_module, root_module['ns3::SocketPriorityTag'])
register_Ns3SocketSetDontFragmentTag_methods(root_module, root_module['ns3::SocketSetDontFragmentTag'])
register_Ns3SpectrumInterference_methods(root_module, root_module['ns3::SpectrumInterference'])
register_Ns3SpectrumModel_methods(root_module, root_module['ns3::SpectrumModel'])
register_Ns3SpectrumPhy_methods(root_module, root_module['ns3::SpectrumPhy'])
register_Ns3SpectrumPropagationLossModel_methods(root_module, root_module['ns3::SpectrumPropagationLossModel'])
register_Ns3SpectrumSignalParameters_methods(root_module, root_module['ns3::SpectrumSignalParameters'])
register_Ns3SpectrumValue_methods(root_module, root_module['ns3::SpectrumValue'])
register_Ns3ThreeLogDistancePropagationLossModel_methods(root_module, root_module['ns3::ThreeLogDistancePropagationLossModel'])
register_Ns3Time_methods(root_module, root_module['ns3::Time'])
register_Ns3TraceParams_methods(root_module, root_module['ns3::TraceParams'])
register_Ns3TraceSourceAccessor_methods(root_module, root_module['ns3::TraceSourceAccessor'])
register_Ns3TracedValue__Ns3Time_methods(root_module, root_module['ns3::TracedValue< ns3::Time >'])
register_Ns3Trailer_methods(root_module, root_module['ns3::Trailer'])
register_Ns3TriangularRandomVariable_methods(root_module, root_module['ns3::TriangularRandomVariable'])
register_Ns3TwoRayGroundPropagationLossModel_methods(root_module, root_module['ns3::TwoRayGroundPropagationLossModel'])
register_Ns3UeManager_methods(root_module, root_module['ns3::UeManager'])
register_Ns3UniformRandomVariable_methods(root_module, root_module['ns3::UniformRandomVariable'])
register_Ns3VendorSpecificValue_methods(root_module, root_module['ns3::VendorSpecificValue'])
register_Ns3WeibullRandomVariable_methods(root_module, root_module['ns3::WeibullRandomVariable'])
register_Ns3ZetaRandomVariable_methods(root_module, root_module['ns3::ZetaRandomVariable'])
register_Ns3ZipfRandomVariable_methods(root_module, root_module['ns3::ZipfRandomVariable'])
register_Ns3ChannelMatrix_methods(root_module, root_module['ns3::channelMatrix'])
register_Ns3MmWaveBeamFormingParams_methods(root_module, root_module['ns3::mmWaveBeamFormingParams'])
register_Ns3MmWaveBeamFormingTraces_methods(root_module, root_module['ns3::mmWaveBeamFormingTraces'])
register_Ns3MmWaveChunkProcessor_methods(root_module, root_module['ns3::MmWaveChunkProcessor'])
register_Ns3MmWaveInterference_methods(root_module, root_module['ns3::mmWaveInterference'])
register_Ns3MmwaveSpectrumSignalParameters_methods(root_module, root_module['ns3::mmwaveSpectrumSignalParameters'])
register_Ns3AntennaModel_methods(root_module, root_module['ns3::AntennaModel'])
register_Ns3Asn1Header_methods(root_module, root_module['ns3::Asn1Header'])
register_Ns3AttributeAccessor_methods(root_module, root_module['ns3::AttributeAccessor'])
register_Ns3AttributeChecker_methods(root_module, root_module['ns3::AttributeChecker'])
register_Ns3AttributeValue_methods(root_module, root_module['ns3::AttributeValue'])
register_Ns3BeamformingParams_methods(root_module, root_module['ns3::BeamformingParams'])
register_Ns3BooleanChecker_methods(root_module, root_module['ns3::BooleanChecker'])
register_Ns3BooleanValue_methods(root_module, root_module['ns3::BooleanValue'])
register_Ns3BoxChecker_methods(root_module, root_module['ns3::BoxChecker'])
register_Ns3BoxValue_methods(root_module, root_module['ns3::BoxValue'])
register_Ns3Building_methods(root_module, root_module['ns3::Building'])
register_Ns3BuildingsPropagationLossModel_methods(root_module, root_module['ns3::BuildingsPropagationLossModel'])
register_Ns3CallbackChecker_methods(root_module, root_module['ns3::CallbackChecker'])
register_Ns3CallbackImplBase_methods(root_module, root_module['ns3::CallbackImplBase'])
register_Ns3CallbackValue_methods(root_module, root_module['ns3::CallbackValue'])
register_Ns3Channel_methods(root_module, root_module['ns3::Channel'])
register_Ns3ChannelParams_methods(root_module, root_module['ns3::ChannelParams'])
register_Ns3CoDelQueueDisc_methods(root_module, root_module['ns3::CoDelQueueDisc'])
register_Ns3ConstantRandomVariable_methods(root_module, root_module['ns3::ConstantRandomVariable'])
register_Ns3CoreNetworkStatsCalculator_methods(root_module, root_module['ns3::CoreNetworkStatsCalculator'])
register_Ns3DataCalculator_methods(root_module, root_module['ns3::DataCalculator'])
register_Ns3DataOutputInterface_methods(root_module, root_module['ns3::DataOutputInterface'])
register_Ns3DataRateChecker_methods(root_module, root_module['ns3::DataRateChecker'])
register_Ns3DataRateValue_methods(root_module, root_module['ns3::DataRateValue'])
register_Ns3DeterministicRandomVariable_methods(root_module, root_module['ns3::DeterministicRandomVariable'])
register_Ns3DoubleValue_methods(root_module, root_module['ns3::DoubleValue'])
register_Ns3EmpiricalRandomVariable_methods(root_module, root_module['ns3::EmpiricalRandomVariable'])
register_Ns3EmptyAttributeAccessor_methods(root_module, root_module['ns3::EmptyAttributeAccessor'])
register_Ns3EmptyAttributeChecker_methods(root_module, root_module['ns3::EmptyAttributeChecker'])
register_Ns3EmptyAttributeValue_methods(root_module, root_module['ns3::EmptyAttributeValue'])
register_Ns3EnumChecker_methods(root_module, root_module['ns3::EnumChecker'])
register_Ns3EnumValue_methods(root_module, root_module['ns3::EnumValue'])
register_Ns3EpcHelper_methods(root_module, root_module['ns3::EpcHelper'])
register_Ns3EpcTft_methods(root_module, root_module['ns3::EpcTft'])
register_Ns3EpcTftPacketFilter_methods(root_module, root_module['ns3::EpcTft::PacketFilter'])
register_Ns3EpcTftClassifier_methods(root_module, root_module['ns3::EpcTftClassifier'])
register_Ns3EpcUeNas_methods(root_module, root_module['ns3::EpcUeNas'])
register_Ns3ErlangRandomVariable_methods(root_module, root_module['ns3::ErlangRandomVariable'])
register_Ns3EventImpl_methods(root_module, root_module['ns3::EventImpl'])
register_Ns3ExponentialRandomVariable_methods(root_module, root_module['ns3::ExponentialRandomVariable'])
register_Ns3FfMacScheduler_methods(root_module, root_module['ns3::FfMacScheduler'])
register_Ns3FixedRssLossModel_methods(root_module, root_module['ns3::FixedRssLossModel'])
register_Ns3FriisPropagationLossModel_methods(root_module, root_module['ns3::FriisPropagationLossModel'])
register_Ns3GammaRandomVariable_methods(root_module, root_module['ns3::GammaRandomVariable'])
register_Ns3IntegerValue_methods(root_module, root_module['ns3::IntegerValue'])
register_Ns3Ipv4_methods(root_module, root_module['ns3::Ipv4'])
register_Ns3Ipv4AddressChecker_methods(root_module, root_module['ns3::Ipv4AddressChecker'])
register_Ns3Ipv4AddressValue_methods(root_module, root_module['ns3::Ipv4AddressValue'])
register_Ns3Ipv4MaskChecker_methods(root_module, root_module['ns3::Ipv4MaskChecker'])
register_Ns3Ipv4MaskValue_methods(root_module, root_module['ns3::Ipv4MaskValue'])
register_Ns3Ipv4MulticastRoute_methods(root_module, root_module['ns3::Ipv4MulticastRoute'])
register_Ns3Ipv4Route_methods(root_module, root_module['ns3::Ipv4Route'])
register_Ns3Ipv6AddressChecker_methods(root_module, root_module['ns3::Ipv6AddressChecker'])
register_Ns3Ipv6AddressValue_methods(root_module, root_module['ns3::Ipv6AddressValue'])
register_Ns3Ipv6PrefixChecker_methods(root_module, root_module['ns3::Ipv6PrefixChecker'])
register_Ns3Ipv6PrefixValue_methods(root_module, root_module['ns3::Ipv6PrefixValue'])
register_Ns3LogDistancePropagationLossModel_methods(root_module, root_module['ns3::LogDistancePropagationLossModel'])
register_Ns3LogNormalRandomVariable_methods(root_module, root_module['ns3::LogNormalRandomVariable'])
register_Ns3LteAmc_methods(root_module, root_module['ns3::LteAmc'])
register_Ns3LteAnr_methods(root_module, root_module['ns3::LteAnr'])
register_Ns3LteControlMessage_methods(root_module, root_module['ns3::LteControlMessage'])
register_Ns3LteEnbMac_methods(root_module, root_module['ns3::LteEnbMac'])
register_Ns3LteEnbRrc_methods(root_module, root_module['ns3::LteEnbRrc'])
register_Ns3LteEnbRrcHandoverEventInfo_methods(root_module, root_module['ns3::LteEnbRrc::HandoverEventInfo'])
register_Ns3LteFfrAlgorithm_methods(root_module, root_module['ns3::LteFfrAlgorithm'])
register_Ns3LteHandoverAlgorithm_methods(root_module, root_module['ns3::LteHandoverAlgorithm'])
register_Ns3LteHarqPhy_methods(root_module, root_module['ns3::LteHarqPhy'])
register_Ns3LteInterference_methods(root_module, root_module['ns3::LteInterference'])
register_Ns3LtePdcp_methods(root_module, root_module['ns3::LtePdcp'])
register_Ns3LtePdcpStatus_methods(root_module, root_module['ns3::LtePdcp::Status'])
register_Ns3LtePhy_methods(root_module, root_module['ns3::LtePhy'])
register_Ns3LteRadioBearerInfo_methods(root_module, root_module['ns3::LteRadioBearerInfo'])
register_Ns3LteRlc_methods(root_module, root_module['ns3::LteRlc'])
register_Ns3LteRlcAm_methods(root_module, root_module['ns3::LteRlcAm'])
register_Ns3LteRlcAmRetxPdu_methods(root_module, root_module['ns3::LteRlcAm::RetxPdu'])
register_Ns3LteRlcSm_methods(root_module, root_module['ns3::LteRlcSm'])
register_Ns3LteSignalingRadioBearerInfo_methods(root_module, root_module['ns3::LteSignalingRadioBearerInfo'])
register_Ns3LteSpectrumPhy_methods(root_module, root_module['ns3::LteSpectrumPhy'])
register_Ns3LteStatsCalculator_methods(root_module, root_module['ns3::LteStatsCalculator'])
register_Ns3LteUeMac_methods(root_module, root_module['ns3::LteUeMac'])
register_Ns3LteUePhy_methods(root_module, root_module['ns3::LteUePhy'])
register_Ns3LteUePowerControl_methods(root_module, root_module['ns3::LteUePowerControl'])
register_Ns3LteUeRrc_methods(root_module, root_module['ns3::LteUeRrc'])
register_Ns3Mac48AddressChecker_methods(root_module, root_module['ns3::Mac48AddressChecker'])
register_Ns3Mac48AddressValue_methods(root_module, root_module['ns3::Mac48AddressValue'])
register_Ns3MatrixPropagationLossModel_methods(root_module, root_module['ns3::MatrixPropagationLossModel'])
register_Ns3McStatsCalculator_methods(root_module, root_module['ns3::McStatsCalculator'])
register_Ns3MibLteControlMessage_methods(root_module, root_module['ns3::MibLteControlMessage'])
register_Ns3MinMaxAvgTotalCalculator__Unsigned_int_methods(root_module, root_module['ns3::MinMaxAvgTotalCalculator< unsigned int >'])
register_Ns3MinMaxAvgTotalCalculator__Unsigned_long_methods(root_module, root_module['ns3::MinMaxAvgTotalCalculator< unsigned long >'])
register_Ns3MmWave3gppBuildingsPropagationLossModel_methods(root_module, root_module['ns3::MmWave3gppBuildingsPropagationLossModel'])
register_Ns3MmWave3gppChannel_methods(root_module, root_module['ns3::MmWave3gppChannel'])
register_Ns3MmWaveAmc_methods(root_module, root_module['ns3::MmWaveAmc'])
register_Ns3MmWaveBeamforming_methods(root_module, root_module['ns3::MmWaveBeamforming'])
register_Ns3MmWaveBearerStatsCalculator_methods(root_module, root_module['ns3::MmWaveBearerStatsCalculator'])
register_Ns3MmWaveBearerStatsConnector_methods(root_module, root_module['ns3::MmWaveBearerStatsConnector'])
register_Ns3MmWaveChannelMatrix_methods(root_module, root_module['ns3::MmWaveChannelMatrix'])
register_Ns3MmWaveChannelRaytracing_methods(root_module, root_module['ns3::MmWaveChannelRaytracing'])
register_Ns3MmWaveControlMessage_methods(root_module, root_module['ns3::MmWaveControlMessage'])
register_Ns3MmWaveDciMessage_methods(root_module, root_module['ns3::MmWaveDciMessage'])
register_Ns3MmWaveDlCqiMessage_methods(root_module, root_module['ns3::MmWaveDlCqiMessage'])
register_Ns3MmWaveDlHarqFeedbackMessage_methods(root_module, root_module['ns3::MmWaveDlHarqFeedbackMessage'])
register_Ns3MmWaveEnbMac_methods(root_module, root_module['ns3::MmWaveEnbMac'])
register_Ns3MmWaveEnbMacReportBufferStatusParameters_methods(root_module, root_module['ns3::MmWaveEnbMac::ReportBufferStatusParameters'])
register_Ns3MmWaveEnbMacTransmitPduParameters_methods(root_module, root_module['ns3::MmWaveEnbMac::TransmitPduParameters'])
register_Ns3MmWaveEnbRrcProtocolIdeal_methods(root_module, root_module['ns3::MmWaveEnbRrcProtocolIdeal'])
register_Ns3MmWaveHarqPhy_methods(root_module, root_module['ns3::MmWaveHarqPhy'])
register_Ns3MmWaveHelper_methods(root_module, root_module['ns3::MmWaveHelper'])
register_Ns3MmWaveLosTracker_methods(root_module, root_module['ns3::MmWaveLosTracker'])
register_Ns3MmWaveLteEnbRrcProtocolReal_methods(root_module, root_module['ns3::MmWaveLteEnbRrcProtocolReal'])
register_Ns3MmWaveLteUeRrcProtocolReal_methods(root_module, root_module['ns3::MmWaveLteUeRrcProtocolReal'])
register_Ns3MmWaveMac_methods(root_module, root_module['ns3::MmWaveMac'])
register_Ns3MmWaveMacScheduler_methods(root_module, root_module['ns3::MmWaveMacScheduler'])
register_Ns3MmWaveMibMessage_methods(root_module, root_module['ns3::MmWaveMibMessage'])
register_Ns3MmWavePhy_methods(root_module, root_module['ns3::MmWavePhy'])
register_Ns3MmWavePhyMacCommon_methods(root_module, root_module['ns3::MmWavePhyMacCommon'])
register_Ns3MmWavePhyRxTrace_methods(root_module, root_module['ns3::MmWavePhyRxTrace'])
register_Ns3MmWavePointToPointEpcHelper_methods(root_module, root_module['ns3::MmWavePointToPointEpcHelper'])
register_Ns3MmWaveRachPreambleMessage_methods(root_module, root_module['ns3::MmWaveRachPreambleMessage'])
register_Ns3MmWaveRarMessage_methods(root_module, root_module['ns3::MmWaveRarMessage'])
register_Ns3MmWaveRarMessageRar_methods(root_module, root_module['ns3::MmWaveRarMessage::Rar'])
register_Ns3MmWaveSib1Message_methods(root_module, root_module['ns3::MmWaveSib1Message'])
register_Ns3MmWaveSpectrumPhy_methods(root_module, root_module['ns3::MmWaveSpectrumPhy'])
register_Ns3MmWaveSpectrumSignalParametersDlCtrlFrame_methods(root_module, root_module['ns3::MmWaveSpectrumSignalParametersDlCtrlFrame'])
register_Ns3MmWaveTdmaDciMessage_methods(root_module, root_module['ns3::MmWaveTdmaDciMessage'])
register_Ns3MmWaveUeMac_methods(root_module, root_module['ns3::MmWaveUeMac'])
register_Ns3MmWaveUePhy_methods(root_module, root_module['ns3::MmWaveUePhy'])
register_Ns3MmWaveUeRrcProtocolIdeal_methods(root_module, root_module['ns3::MmWaveUeRrcProtocolIdeal'])
register_Ns3MmwaveSpectrumSignalParametersDataFrame_methods(root_module, root_module['ns3::MmwaveSpectrumSignalParametersDataFrame'])
register_Ns3MobilityBuildingInfo_methods(root_module, root_module['ns3::MobilityBuildingInfo'])
register_Ns3MobilityModel_methods(root_module, root_module['ns3::MobilityModel'])
register_Ns3NakagamiPropagationLossModel_methods(root_module, root_module['ns3::NakagamiPropagationLossModel'])
register_Ns3NetDevice_methods(root_module, root_module['ns3::NetDevice'])
register_Ns3NixVector_methods(root_module, root_module['ns3::NixVector'])
register_Ns3Node_methods(root_module, root_module['ns3::Node'])
register_Ns3NormalRandomVariable_methods(root_module, root_module['ns3::NormalRandomVariable'])
register_Ns3ObjectFactoryChecker_methods(root_module, root_module['ns3::ObjectFactoryChecker'])
register_Ns3ObjectFactoryValue_methods(root_module, root_module['ns3::ObjectFactoryValue'])
register_Ns3Packet_methods(root_module, root_module['ns3::Packet'])
register_Ns3Params3gpp_methods(root_module, root_module['ns3::Params3gpp'])
register_Ns3ParetoRandomVariable_methods(root_module, root_module['ns3::ParetoRandomVariable'])
register_Ns3PointerChecker_methods(root_module, root_module['ns3::PointerChecker'])
register_Ns3PointerValue_methods(root_module, root_module['ns3::PointerValue'])
register_Ns3QueueItem_methods(root_module, root_module['ns3::QueueItem'])
register_Ns3RachPreambleLteControlMessage_methods(root_module, root_module['ns3::RachPreambleLteControlMessage'])
register_Ns3RarLteControlMessage_methods(root_module, root_module['ns3::RarLteControlMessage'])
register_Ns3RarLteControlMessageRar_methods(root_module, root_module['ns3::RarLteControlMessage::Rar'])
register_Ns3RrcAsn1Header_methods(root_module, root_module['ns3::RrcAsn1Header'])
register_Ns3RrcDlCcchMessage_methods(root_module, root_module['ns3::RrcDlCcchMessage'])
register_Ns3RrcDlDcchMessage_methods(root_module, root_module['ns3::RrcDlDcchMessage'])
register_Ns3RrcUlCcchMessage_methods(root_module, root_module['ns3::RrcUlCcchMessage'])
register_Ns3RrcUlDcchMessage_methods(root_module, root_module['ns3::RrcUlDcchMessage'])
register_Ns3Sib1LteControlMessage_methods(root_module, root_module['ns3::Sib1LteControlMessage'])
register_Ns3SpectrumChannel_methods(root_module, root_module['ns3::SpectrumChannel'])
register_Ns3StringChecker_methods(root_module, root_module['ns3::StringChecker'])
register_Ns3StringValue_methods(root_module, root_module['ns3::StringValue'])
register_Ns3TimeValue_methods(root_module, root_module['ns3::TimeValue'])
register_Ns3TypeIdChecker_methods(root_module, root_module['ns3::TypeIdChecker'])
register_Ns3TypeIdValue_methods(root_module, root_module['ns3::TypeIdValue'])
register_Ns3UintegerValue_methods(root_module, root_module['ns3::UintegerValue'])
register_Ns3UlDciLteControlMessage_methods(root_module, root_module['ns3::UlDciLteControlMessage'])
register_Ns3Vector2DChecker_methods(root_module, root_module['ns3::Vector2DChecker'])
register_Ns3Vector2DValue_methods(root_module, root_module['ns3::Vector2DValue'])
register_Ns3Vector3DChecker_methods(root_module, root_module['ns3::Vector3DChecker'])
register_Ns3Vector3DValue_methods(root_module, root_module['ns3::Vector3DValue'])
register_Ns3AddressChecker_methods(root_module, root_module['ns3::AddressChecker'])
register_Ns3AddressValue_methods(root_module, root_module['ns3::AddressValue'])
register_Ns3AntennaArrayModel_methods(root_module, root_module['ns3::AntennaArrayModel'])
register_Ns3BsrLteControlMessage_methods(root_module, root_module['ns3::BsrLteControlMessage'])
register_Ns3BuildingsObstaclePropagationLossModel_methods(root_module, root_module['ns3::BuildingsObstaclePropagationLossModel'])
register_Ns3CallbackImpl__Bool_Ns3Ptr__lt__ns3NetDevice__gt___Ns3Ptr__lt__const_ns3Packet__gt___Unsigned_short_Const_ns3Address___amp___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< bool, ns3::Ptr<ns3::NetDevice>, ns3::Ptr<const ns3::Packet>, unsigned short, const ns3::Address &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Bool_Ns3Ptr__lt__ns3Socket__gt___Const_ns3Address___amp___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< bool, ns3::Ptr<ns3::Socket>, const ns3::Address &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Ns3ObjectBase___star___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< ns3::ObjectBase *, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Bool_Bool_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, bool, bool, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Const_ns3SpectrumValue___amp___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, const ns3::SpectrumValue &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3EpcUeNasState_Ns3EpcUeNasState_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::EpcUeNas::State, ns3::EpcUeNas::State, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3PhyReceptionStatParameters_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::PhyReceptionStatParameters, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3PhyTransmissionStatParameters_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::PhyTransmissionStatParameters, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3Ptr__lt__const_ns3MobilityModel__gt___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::Ptr<const ns3::MobilityModel>, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3Ptr__lt__const_ns3Packet__gt___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::Ptr<const ns3::Packet>, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3Ptr__lt__const_ns3PacketBurst__gt___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::Ptr<const ns3::PacketBurst>, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3Ptr__lt__const_ns3QueueDiscItem__gt___Const_char___star___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::Ptr<const ns3::QueueDiscItem>, const char *, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3Ptr__lt__const_ns3QueueDiscItem__gt___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::Ptr<const ns3::QueueDiscItem>, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3Ptr__lt__ns3NetDevice__gt___Ns3Ptr__lt__const_ns3Packet__gt___Unsigned_short_Const_ns3Address___amp___Const_ns3Address___amp___Ns3NetDevicePacketType_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::Ptr<ns3::NetDevice>, ns3::Ptr<const ns3::Packet>, unsigned short, const ns3::Address &, const ns3::Address &, ns3::NetDevice::PacketType, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3Ptr__lt__ns3NetDevice__gt___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::Ptr<ns3::NetDevice>, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3Ptr__lt__ns3Packet__gt___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::Ptr<ns3::Packet>, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3Ptr__lt__ns3Socket__gt___Const_ns3Address___amp___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::Ptr<ns3::Socket>, const ns3::Address &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3Ptr__lt__ns3Socket__gt___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::Ptr<ns3::Socket>, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3Ptr__lt__ns3Socket__gt___Unsigned_int_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::Ptr<ns3::Socket>, unsigned int, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3RxPacketTraceParams_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::RxPacketTraceParams, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3Time_Ns3Time_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::Time, ns3::Time, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_int_Unsigned_int_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned int, unsigned int, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_int_Unsigned_int_Unsigned_short_Unsigned_char_Unsigned_short_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned int, unsigned int, unsigned short, unsigned char, unsigned short, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_int_Unsigned_int_Unsigned_short_Unsigned_char_Unsigned_short_Unsigned_char_Unsigned_short_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned int, unsigned int, unsigned short, unsigned char, unsigned short, unsigned char, unsigned short, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_long_Ns3SpectrumValue___amp___Ns3SpectrumValue___amp___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned long, ns3::SpectrumValue &, ns3::SpectrumValue &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_long_Unsigned_long_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned long, unsigned long, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_long_Unsigned_short_Long_double_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned long, unsigned short, long double, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_long_Unsigned_short_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned long, unsigned short, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_long_Unsigned_short_Unsigned_short_Ns3LteRrcSapMeasurementReport_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned long, unsigned short, unsigned short, ns3::LteRrcSap::MeasurementReport, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_long_Unsigned_short_Unsigned_short_Ns3LteUeRrcState_Ns3LteUeRrcState_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned long, unsigned short, unsigned short, ns3::LteUeRrc::State, ns3::LteUeRrc::State, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_long_Unsigned_short_Unsigned_short_Ns3UeManagerState_Ns3UeManagerState_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned long, unsigned short, unsigned short, ns3::UeManager::State, ns3::UeManager::State, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_long_Unsigned_short_Unsigned_short_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned long, unsigned short, unsigned short, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_long_Unsigned_short_Unsigned_short_Unsigned_short_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned long, unsigned short, unsigned short, unsigned short, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_short_Ns3Ptr__lt__ns3SpectrumValue__gt___Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned short, ns3::Ptr<ns3::SpectrumValue>, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_short_Unsigned_char_Unsigned_int_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned short, unsigned char, unsigned int, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_short_Unsigned_char_Unsigned_int_Unsigned_int_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned short, unsigned char, unsigned int, unsigned int, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_short_Unsigned_char_Unsigned_int_Unsigned_long_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned short, unsigned char, unsigned int, unsigned long, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_short_Unsigned_short_Double_Double_Bool_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned short, unsigned short, double, double, bool, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_short_Unsigned_short_Double_Double_Unsigned_char_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned short, unsigned short, double, double, unsigned char, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_short_Unsigned_short_Double_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned short, unsigned short, double, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_short_Unsigned_short_Double_Unsigned_char_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned short, unsigned short, double, unsigned char, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_short_Unsigned_short_Ns3LteUePhyState_Ns3LteUePhyState_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned short, unsigned short, ns3::LteUePhy::State, ns3::LteUePhy::State, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_short_Unsigned_short_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned short, unsigned short, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3CallbackImpl__Void_Unsigned_short_Unsigned_short_Unsigned_int_Unsigned_char_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_Ns3Empty_methods(root_module, root_module['ns3::CallbackImpl< void, unsigned short, unsigned short, unsigned int, unsigned char, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >'])
register_Ns3DlCqiLteControlMessage_methods(root_module, root_module['ns3::DlCqiLteControlMessage'])
register_Ns3DlDciLteControlMessage_methods(root_module, root_module['ns3::DlDciLteControlMessage'])
register_Ns3DlHarqFeedbackLteControlMessage_methods(root_module, root_module['ns3::DlHarqFeedbackLteControlMessage'])
register_Ns3HandoverPreparationInfoHeader_methods(root_module, root_module['ns3::HandoverPreparationInfoHeader'])
register_Ns3LteDataRadioBearerInfo_methods(root_module, root_module['ns3::LteDataRadioBearerInfo'])
register_Ns3LteEnbPhy_methods(root_module, root_module['ns3::LteEnbPhy'])
register_Ns3LteNetDevice_methods(root_module, root_module['ns3::LteNetDevice'])
register_Ns3McUeNetDevice_methods(root_module, root_module['ns3::McUeNetDevice'])
register_Ns3MeasurementReportHeader_methods(root_module, root_module['ns3::MeasurementReportHeader'])
register_Ns3MmWaveBsrMessage_methods(root_module, root_module['ns3::MmWaveBsrMessage'])
register_Ns3MmWaveEnbPhy_methods(root_module, root_module['ns3::MmWaveEnbPhy'])
register_Ns3MmWaveFlexTtiMacScheduler_methods(root_module, root_module['ns3::MmWaveFlexTtiMacScheduler'])
register_Ns3MmWaveFlexTtiMaxRateMacScheduler_methods(root_module, root_module['ns3::MmWaveFlexTtiMaxRateMacScheduler'])
register_Ns3MmWaveFlexTtiMaxWeightMacScheduler_methods(root_module, root_module['ns3::MmWaveFlexTtiMaxWeightMacScheduler'])
register_Ns3MmWaveFlexTtiPfMacScheduler_methods(root_module, root_module['ns3::MmWaveFlexTtiPfMacScheduler'])
register_Ns3MmWaveNetDevice_methods(root_module, root_module['ns3::MmWaveNetDevice'])
register_Ns3MmWaveUeNetDevice_methods(root_module, root_module['ns3::MmWaveUeNetDevice'])
register_Ns3QueueDiscItem_methods(root_module, root_module['ns3::QueueDiscItem'])
register_Ns3RrcConnectToMmWaveHeader_methods(root_module, root_module['ns3::RrcConnectToMmWaveHeader'])
register_Ns3RrcConnectionReconfigurationCompleteHeader_methods(root_module, root_module['ns3::RrcConnectionReconfigurationCompleteHeader'])
register_Ns3RrcConnectionReconfigurationHeader_methods(root_module, root_module['ns3::RrcConnectionReconfigurationHeader'])
register_Ns3RrcConnectionReestablishmentCompleteHeader_methods(root_module, root_module['ns3::RrcConnectionReestablishmentCompleteHeader'])
register_Ns3RrcConnectionReestablishmentHeader_methods(root_module, root_module['ns3::RrcConnectionReestablishmentHeader'])
register_Ns3RrcConnectionReestablishmentRejectHeader_methods(root_module, root_module['ns3::RrcConnectionReestablishmentRejectHeader'])
register_Ns3RrcConnectionReestablishmentRequestHeader_methods(root_module, root_module['ns3::RrcConnectionReestablishmentRequestHeader'])
register_Ns3RrcConnectionRejectHeader_methods(root_module, root_module['ns3::RrcConnectionRejectHeader'])
register_Ns3RrcConnectionReleaseHeader_methods(root_module, root_module['ns3::RrcConnectionReleaseHeader'])
register_Ns3RrcConnectionRequestHeader_methods(root_module, root_module['ns3::RrcConnectionRequestHeader'])
register_Ns3RrcConnectionSetupCompleteHeader_methods(root_module, root_module['ns3::RrcConnectionSetupCompleteHeader'])
register_Ns3RrcConnectionSetupHeader_methods(root_module, root_module['ns3::RrcConnectionSetupHeader'])
register_Ns3RrcConnectionSwitchHeader_methods(root_module, root_module['ns3::RrcConnectionSwitchHeader'])
register_Ns3RrcNotifySecondaryConnectedHeader_methods(root_module, root_module['ns3::RrcNotifySecondaryConnectedHeader'])
register_Ns3LteEnbNetDevice_methods(root_module, root_module['ns3::LteEnbNetDevice'])
register_Ns3MmWaveEnbNetDevice_methods(root_module, root_module['ns3::MmWaveEnbNetDevice'])
register_Ns3ConfigMatchContainer_methods(root_module, root_module['ns3::Config::MatchContainer'])
register_Ns3HashImplementation_methods(root_module, root_module['ns3::Hash::Implementation'])
register_Ns3HashFunctionFnv1a_methods(root_module, root_module['ns3::Hash::Function::Fnv1a'])
register_Ns3HashFunctionHash32_methods(root_module, root_module['ns3::Hash::Function::Hash32'])
register_Ns3HashFunctionHash64_methods(root_module, root_module['ns3::Hash::Function::Hash64'])
register_Ns3HashFunctionMurmur3_methods(root_module, root_module['ns3::Hash::Function::Murmur3'])
return |
def visualize_generic_dataset(base_dir, dataset_json):
from stemseg.utils.vis import overlay_mask_on_image, create_color_map
(seqs, meta_info) = parse_generic_video_dataset(base_dir, dataset_json)
category_names = meta_info['category_labels']
cmap = create_color_map().tolist()
cv2.namedWindow('Image', cv2.WINDOW_NORMAL)
for seq in seqs:
if (len(seq) > 100):
frame_idxes = list(range(100, 150))
else:
frame_idxes = None
images = seq.load_images(frame_idxes)
masks = seq.load_masks(frame_idxes)
category_labels = seq.category_labels
print('[COLOR NAME] -> [CATEGORY NAME]')
color_key_printed = False
for (image_t, masks_t) in zip(images, masks):
for (i, (mask, cat_label)) in enumerate(zip(masks_t, category_labels), 1):
image_t = overlay_mask_on_image(image_t, mask, mask_color=cmap[i])
if (not color_key_printed):
print('{} -> {}'.format(cmap[i], category_names[cat_label]))
color_key_printed = True
cv2.imshow('Image', image_t)
if (cv2.waitKey(0) == 113):
exit(0) |
def normalize_vertices(pc):
centroid = np.mean(pc, axis=0)
pc = (pc - centroid)
m = np.max(np.sqrt(np.sum((pc ** 2), axis=1)))
pc = (pc / m)
return pc |
def get_lstm_grad(xs_np, h0_np, c0_np, w0_np, w_np, b_np, dy, dh, dc, num_layers=1, dropout=0.0, bidirectional=False, training=True, **kw):
num_directions = (2 if bidirectional else 1)
seq_len = xs_np.shape[0]
batch_size = xs_np.shape[1]
hidden_size = h0_np.shape[3]
xs = nn.Variable.from_numpy_array(xs_np, need_grad=True)
h0 = nn.Variable.from_numpy_array(h0_np, need_grad=True)
c0 = nn.Variable.from_numpy_array(c0_np, need_grad=True)
w0 = nn.Variable.from_numpy_array(w0_np, need_grad=True)
w = None
b = None
with_bias = False
if (num_layers > 1):
w = nn.Variable.from_numpy_array(w_np, need_grad=True)
if (type(b_np) == np.ndarray):
b = nn.Variable.from_numpy_array(b_np, need_grad=True)
with_bias = True
xs.grad.zero()
h0.grad.zero()
c0.grad.zero()
w0.grad.zero()
if (num_layers > 1):
w.grad.zero()
if with_bias:
b.grad.zero()
(ys, hn, cn) = _create_fixed_length_lstm(xs, h0, c0, w0, w, b, num_layers, num_directions, with_bias)
dummy = F.sink(ys, hn, cn, one_input_grad=False)
dummy.forward()
ys.g = np.reshape(dy, ys.shape)
hn.g = dh
cn.g = dc
dummy.backward()
if ((num_layers > 1) and with_bias):
return np.concatenate((xs.g.flat, h0.g.flat, c0.g.flat, w0.g.flat, w.g.flat, b.g.flat))
elif ((num_layers > 1) and (not with_bias)):
return np.concatenate((xs.g.flat, h0.g.flat, c0.g.flat, w0.g.flat, w.g.flat))
elif ((num_layers == 1) and with_bias):
return np.concatenate((xs.g.flat, h0.g.flat, c0.g.flat, w0.g.flat, b.g.flat))
else:
return np.concatenate((xs.g.flat, h0.g.flat, c0.g.flat, w0.g.flat)) |
def main(_):
with open(FLAGS.vocab, 'r') as lines:
orig_vocab_sz = sum((1 for _ in lines))
shard_sz = FLAGS.shard_size
vocab_sz = (orig_vocab_sz - (orig_vocab_sz % shard_sz))
nshards = (vocab_sz / shard_sz)
print(('vocab size is %d (originally %d), %d %dx%d-element shards' % (vocab_sz, orig_vocab_sz, (nshards * nshards), shard_sz, shard_sz)))
if (FLAGS.output_dir and (not os.path.isdir(FLAGS.output_dir))):
os.makedirs(FLAGS.output_dir)
with open(FLAGS.input, 'r') as coocs:
(shard_files, marginals) = make_shard_files(coocs, nshards, vocab_sz)
filename = os.path.join(FLAGS.output_dir, 'shards.recs')
with tf.python_io.TFRecordWriter(filename) as writer:
ix = 0
for ((row, col), fh) in shard_files.iteritems():
ix += 1
sys.stdout.write(('\rwriting shard %d/%d' % (ix, len(shard_files))))
sys.stdout.flush()
fh.seek(0)
buf = fh.read()
os.unlink(fh.name)
fh.close()
coocs = [shard_cooc_fmt.unpack_from(buf, off) for off in range(0, len(buf), shard_cooc_fmt.size)]
coocs.sort(key=(lambda kv: kv[0]))
def _int64s(xs):
return tf.train.Feature(int64_list=tf.train.Int64List(value=list(xs)))
def _floats(xs):
return tf.train.Feature(float_list=tf.train.FloatList(value=list(xs)))
example = tf.train.Example(features=tf.train.Features(feature={'global_row': _int64s(((row + (nshards * i)) for i in range(shard_sz))), 'global_col': _int64s(((col + (nshards * i)) for i in range(shard_sz))), 'sparse_local_row': _int64s(((pos / shard_sz) for (pos, _) in coocs)), 'sparse_local_col': _int64s(((pos % shard_sz) for (pos, _) in coocs)), 'sparse_value': _floats((cnt for (_, cnt) in coocs))}))
writer.write(example.SerializeToString())
print('\nwriting marginals...')
with open(os.path.join(FLAGS.output_dir, 'marginals.txt'), 'w') as fh:
for cnt in marginals:
fh.write(('%0.1f\n' % cnt))
print('done!') |
def test_find_spans():
raw = ['u', 'n', 'b', 'a', 'n', ' ', 'm', 'o', 'x', ' ', 'o', 'p', 'a', 'l']
assert (utils.find_spans(raw) == [(0, 14)])
raw = ['u', 'n', 'b', 'a', 'n', ' ', 'm', 'o', 'x', ' ', 'o', 'p', 'a', 'l', '<PAD>']
assert (utils.find_spans(raw) == [(0, 14)])
raw = ['<PAD>', 'u', 'n', 'b', 'a', 'n', ' ', 'm', 'o', 'x', ' ', 'o', 'p', 'a', 'l', '<PAD>']
assert (utils.find_spans(raw) == [(1, 15)])
raw = ['<PAD>', 'u', 'n', 'b', 'a', 'n', ' ', 'm', 'o', 'x', ' ', 'o', 'p', 'a', 'l']
assert (utils.find_spans(raw) == [(1, 15)])
raw = ['<PAD>', 'u', 'n', 'b', 'a', 'n', '<PAD>', 'm', 'o', 'x', ' ', 'o', 'p', 'a', 'l']
assert (utils.find_spans(raw) == [(1, 6), (7, 15)]) |
def hcopy(from_path: str, to_path: str) -> bool:
if to_path.startswith('hdfs'):
if from_path.startswith('hdfs'):
os.system('{} dfs -cp -f {} {}'.format(HADOOP_BIN, from_path, to_path))
else:
os.system('{} dfs -copyFromLocal -f {} {}'.format(HADOOP_BIN, from_path, to_path))
elif from_path.startswith('hdfs'):
os.system('{} dfs -text {} > {}'.format(HADOOP_BIN, from_path, to_path))
else:
shutil.copy(from_path, to_path)
return True |
.expansion
class ExpandIrecvMPI(ExpandTransformation):
environments = [environments.mpi.MPI]
def expansion(node, parent_state, parent_sdfg, n=None, **kwargs):
((buffer, count_str, buffer_offset, ddt), src, tag) = node.validate(parent_sdfg, parent_state)
mpi_dtype_str = dace.libraries.mpi.utils.MPI_DDT(buffer.dtype.base_type)
if (buffer.dtype.veclen > 1):
raise NotImplementedError
comm = 'MPI_COMM_WORLD'
if node.grid:
comm = f'__state->{node.grid}_comm'
code = ''
if (ddt is not None):
code = f'''static MPI_Datatype newtype;
static int init=1;
if (init) {{
MPI_Type_vector({ddt['count']}, {ddt['blocklen']}, {ddt['stride']}, {ddt['oldtype']}, &newtype);
MPI_Type_commit(&newtype);
init = 0;
}}
'''
mpi_dtype_str = 'newtype'
count_str = '1'
buffer_offset = 0
code += f'MPI_Irecv(_buffer, {count_str}, {mpi_dtype_str}, int(_src), int(_tag), {comm}, _request);'
if (ddt is not None):
code += f'''// MPI_Type_free(&newtype);
'''
tasklet = dace.sdfg.nodes.Tasklet(node.name, node.in_connectors, node.out_connectors, code, language=dace.dtypes.Language.CPP)
conn = tasklet.out_connectors
conn = {c: (dtypes.pointer(dtypes.opaque('MPI_Request')) if (c == '_request') else t) for (c, t) in conn.items()}
tasklet.out_connectors = conn
return tasklet |
class ShapeNetCoreTFRecordWriter():
def __init__(self, object_category: str='Airplane', n_sampled_points: int=1024, viz_samples=None) -> None:
self.dataset_path = '/tmp/.keras/datasets/PartAnnotation'
if ((not os.path.exists(self.dataset_path)) or (os.listdir(self.dataset_path) == 0)):
self._get_files()
self.metadata = self._load_metadata()
if (object_category not in self.metadata.keys()):
raise KeyError(('Not a valid Shapenet Object. Must be one of ' + str(self.metadata.keys())))
else:
self.object_category = object_category
self.viz_samples = viz_samples
self.n_sampled_points = n_sampled_points
(self.point_clouds, self.test_point_clouds) = ([], [])
(self.point_cloud_labels, self.point_cloud_dataframes) = ([], [])
self.labels = self.metadata[self.object_category]['lables']
self.colors = self.metadata[self.object_category]['colors']
def _get_files(self):
dataset_url = '
keras.utils.get_file(fname='shapenet.zip', origin=dataset_url, cache_subdir='datasets', hash_algorithm='auto', extract=True, archive_format='auto', cache_dir='datasets')
def _load_metadata(self):
with open(os.path.join(self.dataset_path, 'metadata.json')) as json_file:
metadata = json.load(json_file)
return metadata
def _sample_point_clouds(self):
for index in tqdm(range(len(self.point_clouds))):
current_point_cloud = self.point_clouds[index]
current_label_cloud = self.point_cloud_labels[index]
n_points = len(current_point_cloud)
sampled_indices = random.sample(list(range(n_points)), self.n_sampled_points)
sampled_point_cloud = np.array([current_point_cloud[i] for i in sampled_indices])
sampled_label_cloud = np.array([current_label_cloud[i] for i in sampled_indices])
norm_point_cloud = (sampled_point_cloud - np.mean(sampled_point_cloud, axis=0))
norm_point_cloud /= np.max(np.linalg.norm(norm_point_cloud, axis=1))
self.point_clouds[index] = sampled_point_cloud
self.point_cloud_labels[index] = sampled_label_cloud
def load_data(self, limit=None) -> None:
points_dir = os.path.join(self.dataset_path, '{}/points'.format(self.metadata[self.object_category]['directory']))
labels_dir = os.path.join(self.dataset_path, '{}/points_label'.format(self.metadata[self.object_category]['directory']))
points_files = glob(os.path.join(points_dir, '*.pts'))
if (limit is not None):
points_files = points_files[:limit]
for point_file in tqdm(points_files):
point_cloud = np.loadtxt(point_file)
if (point_cloud.shape[0] < self.n_sampled_points):
continue
file_id = point_file.split('/')[(- 1)].split('.')[0]
(label_data, num_labels) = ({}, 0)
for label in self.labels:
label_file = os.path.join(labels_dir, label, (file_id + '.seg'))
if os.path.exists(label_file):
label_data[label] = np.loadtxt(label_file).astype('float32')
num_labels = len(label_data[label])
try:
label_map = (['none'] * num_labels)
for label in self.labels:
for (i, data) in enumerate(label_data[label]):
label_map[i] = (label if (data == 1) else label_map[i])
label_data = [(self.labels.index(label) if (label != 'none') else len(self.labels)) for label in label_map]
label_data = keras.utils.to_categorical(label_data, num_classes=(len(self.labels) + 1))
self.point_clouds.append(point_cloud)
self.point_cloud_labels.append(label_data)
except KeyError:
self.test_point_clouds.append(point_cloud)
self._sample_point_clouds()
def _write_tfrecords_with_labels(self, point_clouds, label_clouds, samples_per_shard: int, tfrecord_dir: str, split: str):
num_tfrecords = (len(point_clouds) // samples_per_shard)
if (len(point_clouds) % samples_per_shard):
num_tfrecords += 1
logging.info(f'num_tfrecords: {num_tfrecords}')
point_cloud_shards = split_list(point_clouds, samples_per_shard)
label_cloud_shards = split_list(label_clouds, samples_per_shard)
(lower_limit, upper_limit) = (0, samples_per_shard)
for index in range(num_tfrecords):
point_cloud_shard = point_cloud_shards[index]
label_cloud_shard = label_cloud_shards[index]
file_name = 'shapenet-{}-{}-{:04d}-{:04d}.tfrec'.format(self.n_sampled_points, split, lower_limit, upper_limit)
lower_limit += samples_per_shard
upper_limit += samples_per_shard
logging.info(f'Writing TFRecord File {file_name}')
with tf.io.TFRecordWriter(os.path.join(tfrecord_dir, self.object_category, split, file_name)) as writer:
for sample_index in tqdm(range(len(point_cloud_shard))):
example = tf.train.Example(features=tf.train.Features(feature=create_example(point_cloud_shard[sample_index], label_cloud_shard[sample_index])))
writer.write(example.SerializeToString())
def write_tfrecords(self, val_split: float, samples_per_shard: int, tfrecord_dir: str):
make_dir(tfrecord_dir)
make_dir(os.path.join(tfrecord_dir, self.object_category))
make_dir(os.path.join(tfrecord_dir, self.object_category, 'train'))
make_dir(os.path.join(tfrecord_dir, self.object_category, 'val'))
split_index = int((len(self.point_clouds) * (1 - val_split)))
train_point_clouds = self.point_clouds[:split_index]
train_label_clouds = self.point_cloud_labels[:split_index]
val_point_clouds = self.point_clouds[split_index:]
val_label_clouds = self.point_cloud_labels[split_index:]
logging.info('Creating Train TFRecords...')
self._write_tfrecords_with_labels(train_point_clouds, train_label_clouds, samples_per_shard, tfrecord_dir, 'train')
logging.info('Creating Validation TFRecords...')
self._write_tfrecords_with_labels(val_point_clouds, val_label_clouds, samples_per_shard, tfrecord_dir, 'val') |
def invalid_rate(value: str) -> UsageError:
return UsageError(f'Invalid rate limit value: `{value}`. Should be in form `limit/interval`. Example: `10/m` for 10 requests per minute.') |
def _draw_rect(img, rect, color, thickness=2):
p1 = (int(rect[0]), int(rect[1]))
p2 = (int((rect[0] + rect[2])), int((rect[1] + rect[3])))
cv2.rectangle(img, p1, p2, color, thickness) |
def add_arguments_data(parser):
parser.add_argument('--random-crop', type=int, default=2)
parser.add_argument('--num-class', type=int, default=10)
parser.add_argument('--no-data-aug', action='store_true')
parser.add_argument('--test-batch-size', type=int)
parser.add_argument('--batch-size', type=int) |
def _kmeans_plusplus(X, n_clusters, x_squared_norms, sample_weight, random_state, n_local_trials=None):
(n_samples, n_features) = X.shape
centers = np.empty((n_clusters, n_features), dtype=X.dtype)
if (n_local_trials is None):
n_local_trials = (2 + int(np.log(n_clusters)))
center_id = random_state.choice(n_samples, p=(sample_weight / sample_weight.sum()))
indices = np.full(n_clusters, (- 1), dtype=int)
if sp.issparse(X):
centers[0] = X[[center_id]].toarray()
else:
centers[0] = X[center_id]
indices[0] = center_id
closest_dist_sq = _euclidean_distances(centers[(0, np.newaxis)], X, Y_norm_squared=x_squared_norms, squared=True)
current_pot = (closest_dist_sq sample_weight)
for c in range(1, n_clusters):
rand_vals = (random_state.uniform(size=n_local_trials) * current_pot)
candidate_ids = np.searchsorted(stable_cumsum((sample_weight * closest_dist_sq)), rand_vals)
np.clip(candidate_ids, None, (closest_dist_sq.size - 1), out=candidate_ids)
distance_to_candidates = _euclidean_distances(X[candidate_ids], X, Y_norm_squared=x_squared_norms, squared=True)
np.minimum(closest_dist_sq, distance_to_candidates, out=distance_to_candidates)
candidates_pot = (distance_to_candidates sample_weight.reshape((- 1), 1))
best_candidate = np.argmin(candidates_pot)
current_pot = candidates_pot[best_candidate]
closest_dist_sq = distance_to_candidates[best_candidate]
best_candidate = candidate_ids[best_candidate]
if sp.issparse(X):
centers[c] = X[[best_candidate]].toarray()
else:
centers[c] = X[best_candidate]
indices[c] = best_candidate
return (centers, indices) |
def detect_file_discrepancies(session_name, data_dir):
logger = logging.getLogger(__name__)
base_dir = osp.join(data_dir, session_name)
for object_dir in next(os.walk(base_dir))[1]:
object_dir = osp.join(base_dir, object_dir)
object_name_filename = osp.join(object_dir, 'object_name.txt')
try:
with open(object_name_filename, 'r') as f:
object_name = f.readline().strip()
except IOError:
logger.warn('Skipping {:s}'.format(object_dir))
continue
pose_dir = osp.join(object_dir, 'poses')
pose_count = 0
for fname in next(os.walk(pose_dir))[(- 1)]:
if ('camera_pose' not in fname):
continue
pose_count += 1
thermal_dir = osp.join(object_dir, 'thermal_images')
thermal_count = 0
for fname in next(os.walk(thermal_dir))[(- 1)]:
if (('.png' not in fname) or ('mask' in fname)):
continue
thermal_count += 1
if (pose_count != thermal_count):
logger.warning('{:s} has unequal number of poses and thermal images'.format(object_dir)) |
def load_local_or_remote_file(filepath, file_type=None):
local_path = local_path_from_s3_or_local_path(filepath)
if (local_path is None):
return None
if (file_type is None):
extension = local_path.split('.')[(- 1)]
if (extension == 'npy'):
file_type = NUMPY
else:
file_type = PICKLE
else:
file_type = PICKLE
if (file_type == NUMPY):
object = np.load(open(local_path, 'rb'))
elif (file_type == JOBLIB):
object = joblib.load(local_path)
else:
object = pickle.load(open(local_path, 'rb'))
print('loaded', local_path)
return object |
class ExtraCloseTreeError(ValueError):
def __init__(self, line_num):
super().__init__(('Found a broken tree (extra close brackets). Tree started on line %d' % line_num))
self.line_num = line_num |
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